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You searched for subject:(Graph mining). Showing records 1 – 30 of 174 total matches.

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Louisiana State University

1. Jose, Neha Clare. Social Media Network Data Mining and Optimization.

Degree: MS, Computer Sciences, 2016, Louisiana State University

 Many small social aid organizations could benefit from collaborating with other organizations on common causes, but may not have the necessary social relationships. We present… (more)

Subjects/Keywords: Graph Analysis; Centrality; Data Mining

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APA (6th Edition):

Jose, N. C. (2016). Social Media Network Data Mining and Optimization. (Masters Thesis). Louisiana State University. Retrieved from etd-05252016-161222 ; https://digitalcommons.lsu.edu/gradschool_theses/3024

Chicago Manual of Style (16th Edition):

Jose, Neha Clare. “Social Media Network Data Mining and Optimization.” 2016. Masters Thesis, Louisiana State University. Accessed August 07, 2020. etd-05252016-161222 ; https://digitalcommons.lsu.edu/gradschool_theses/3024.

MLA Handbook (7th Edition):

Jose, Neha Clare. “Social Media Network Data Mining and Optimization.” 2016. Web. 07 Aug 2020.

Vancouver:

Jose NC. Social Media Network Data Mining and Optimization. [Internet] [Masters thesis]. Louisiana State University; 2016. [cited 2020 Aug 07]. Available from: etd-05252016-161222 ; https://digitalcommons.lsu.edu/gradschool_theses/3024.

Council of Science Editors:

Jose NC. Social Media Network Data Mining and Optimization. [Masters Thesis]. Louisiana State University; 2016. Available from: etd-05252016-161222 ; https://digitalcommons.lsu.edu/gradschool_theses/3024


Anna University

2. Sabeen S. Association rule mining using directed graphs and hypergraphs;.

Degree: Association rule mining using directed graphs and hypergraphs, 2015, Anna University

Data mining is a potential tool for prolific analysis of data The task of association rule mining in a large database is one of the… (more)

Subjects/Keywords: Data mining; Directed graph model

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APA (6th Edition):

S, S. (2015). Association rule mining using directed graphs and hypergraphs;. (Thesis). Anna University. Retrieved from http://shodhganga.inflibnet.ac.in/handle/10603/40129

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Chicago Manual of Style (16th Edition):

S, Sabeen. “Association rule mining using directed graphs and hypergraphs;.” 2015. Thesis, Anna University. Accessed August 07, 2020. http://shodhganga.inflibnet.ac.in/handle/10603/40129.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

MLA Handbook (7th Edition):

S, Sabeen. “Association rule mining using directed graphs and hypergraphs;.” 2015. Web. 07 Aug 2020.

Vancouver:

S S. Association rule mining using directed graphs and hypergraphs;. [Internet] [Thesis]. Anna University; 2015. [cited 2020 Aug 07]. Available from: http://shodhganga.inflibnet.ac.in/handle/10603/40129.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Council of Science Editors:

S S. Association rule mining using directed graphs and hypergraphs;. [Thesis]. Anna University; 2015. Available from: http://shodhganga.inflibnet.ac.in/handle/10603/40129

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

3. Zhang, Yao. Optimizing and Understanding Network Structure for Diffusion.

Degree: PhD, Computer Science, 2017, Virginia Tech

 Given a population contact network and electronic medical records of patients, how to distribute vaccines to individuals to effectively control a flu epidemic? Similarly, given… (more)

Subjects/Keywords: Data Mining; Graph/Network; Diffusion

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APA (6th Edition):

Zhang, Y. (2017). Optimizing and Understanding Network Structure for Diffusion. (Doctoral Dissertation). Virginia Tech. Retrieved from http://hdl.handle.net/10919/79674

Chicago Manual of Style (16th Edition):

Zhang, Yao. “Optimizing and Understanding Network Structure for Diffusion.” 2017. Doctoral Dissertation, Virginia Tech. Accessed August 07, 2020. http://hdl.handle.net/10919/79674.

MLA Handbook (7th Edition):

Zhang, Yao. “Optimizing and Understanding Network Structure for Diffusion.” 2017. Web. 07 Aug 2020.

Vancouver:

Zhang Y. Optimizing and Understanding Network Structure for Diffusion. [Internet] [Doctoral dissertation]. Virginia Tech; 2017. [cited 2020 Aug 07]. Available from: http://hdl.handle.net/10919/79674.

Council of Science Editors:

Zhang Y. Optimizing and Understanding Network Structure for Diffusion. [Doctoral Dissertation]. Virginia Tech; 2017. Available from: http://hdl.handle.net/10919/79674


Rutgers University

4. Kang, Yunyi, 1991-. Anomaly detection in network using non-negative matrix factorization techniques.

Degree: MS, Industrial and Systems Engineering, 2015, Rutgers University

 Anomaly detection is becoming an important problem in graph mining. This is because people are eager to find out unusual objects or patterns in a… (more)

Subjects/Keywords: Graph theory; Anomalies; Data mining

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APA (6th Edition):

Kang, Yunyi, 1. (2015). Anomaly detection in network using non-negative matrix factorization techniques. (Masters Thesis). Rutgers University. Retrieved from https://rucore.libraries.rutgers.edu/rutgers-lib/48536/

Chicago Manual of Style (16th Edition):

Kang, Yunyi, 1991-. “Anomaly detection in network using non-negative matrix factorization techniques.” 2015. Masters Thesis, Rutgers University. Accessed August 07, 2020. https://rucore.libraries.rutgers.edu/rutgers-lib/48536/.

MLA Handbook (7th Edition):

Kang, Yunyi, 1991-. “Anomaly detection in network using non-negative matrix factorization techniques.” 2015. Web. 07 Aug 2020.

Vancouver:

Kang, Yunyi 1. Anomaly detection in network using non-negative matrix factorization techniques. [Internet] [Masters thesis]. Rutgers University; 2015. [cited 2020 Aug 07]. Available from: https://rucore.libraries.rutgers.edu/rutgers-lib/48536/.

Council of Science Editors:

Kang, Yunyi 1. Anomaly detection in network using non-negative matrix factorization techniques. [Masters Thesis]. Rutgers University; 2015. Available from: https://rucore.libraries.rutgers.edu/rutgers-lib/48536/


Washington State University

5. [No author]. Query-driven Exploration of Big Graphs .

Degree: 2019, Washington State University

 Exploring graph-structured data either by mining or querying is a fundamental operation that enables important applications including knowledge graph search, social network analysis, and cyber-network… (more)

Subjects/Keywords: Computer science; Exploratory Search; Graph Database; Graph Exploration; Graph Mining; Graph Querying; Graph Streams

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APA (6th Edition):

author], [. (2019). Query-driven Exploration of Big Graphs . (Thesis). Washington State University. Retrieved from http://hdl.handle.net/2376/17891

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Chicago Manual of Style (16th Edition):

author], [No. “Query-driven Exploration of Big Graphs .” 2019. Thesis, Washington State University. Accessed August 07, 2020. http://hdl.handle.net/2376/17891.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

MLA Handbook (7th Edition):

author], [No. “Query-driven Exploration of Big Graphs .” 2019. Web. 07 Aug 2020.

Vancouver:

author] [. Query-driven Exploration of Big Graphs . [Internet] [Thesis]. Washington State University; 2019. [cited 2020 Aug 07]. Available from: http://hdl.handle.net/2376/17891.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Council of Science Editors:

author] [. Query-driven Exploration of Big Graphs . [Thesis]. Washington State University; 2019. Available from: http://hdl.handle.net/2376/17891

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation


IUPUI

6. Mandal, Aritra. Distributed graph decomposition algorithms on Apache Spark.

Degree: 2018, IUPUI

Indiana University-Purdue University Indianapolis (IUPUI)

Structural analysis and mining of large and complex graphs for describing the characteristics of a vertex or an edge in… (more)

Subjects/Keywords: Graph Mining; Big Data; Apache Spark; Graph Decomposition; Graph Partitioning; Clustering

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APA (6th Edition):

Mandal, A. (2018). Distributed graph decomposition algorithms on Apache Spark. (Thesis). IUPUI. Retrieved from http://hdl.handle.net/1805/16924

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Chicago Manual of Style (16th Edition):

Mandal, Aritra. “Distributed graph decomposition algorithms on Apache Spark.” 2018. Thesis, IUPUI. Accessed August 07, 2020. http://hdl.handle.net/1805/16924.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

MLA Handbook (7th Edition):

Mandal, Aritra. “Distributed graph decomposition algorithms on Apache Spark.” 2018. Web. 07 Aug 2020.

Vancouver:

Mandal A. Distributed graph decomposition algorithms on Apache Spark. [Internet] [Thesis]. IUPUI; 2018. [cited 2020 Aug 07]. Available from: http://hdl.handle.net/1805/16924.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Council of Science Editors:

Mandal A. Distributed graph decomposition algorithms on Apache Spark. [Thesis]. IUPUI; 2018. Available from: http://hdl.handle.net/1805/16924

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation


University of Alberta

7. Simoes Gomes, Carolina. Heavyweight Pattern Mining in Attributed Flow Graphs.

Degree: MS, Department of Computing Science, 2012, University of Alberta

 Flow graphs are an abstraction used to represent elements travelling through a network of nodes. The paths between nodes are directed edges in the graph,… (more)

Subjects/Keywords: software analysis; pattern mining; program analysis; flow graph; program profiling; sub-graph mining; data mining

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APA (6th Edition):

Simoes Gomes, C. (2012). Heavyweight Pattern Mining in Attributed Flow Graphs. (Masters Thesis). University of Alberta. Retrieved from https://era.library.ualberta.ca/files/r494vk17h

Chicago Manual of Style (16th Edition):

Simoes Gomes, Carolina. “Heavyweight Pattern Mining in Attributed Flow Graphs.” 2012. Masters Thesis, University of Alberta. Accessed August 07, 2020. https://era.library.ualberta.ca/files/r494vk17h.

MLA Handbook (7th Edition):

Simoes Gomes, Carolina. “Heavyweight Pattern Mining in Attributed Flow Graphs.” 2012. Web. 07 Aug 2020.

Vancouver:

Simoes Gomes C. Heavyweight Pattern Mining in Attributed Flow Graphs. [Internet] [Masters thesis]. University of Alberta; 2012. [cited 2020 Aug 07]. Available from: https://era.library.ualberta.ca/files/r494vk17h.

Council of Science Editors:

Simoes Gomes C. Heavyweight Pattern Mining in Attributed Flow Graphs. [Masters Thesis]. University of Alberta; 2012. Available from: https://era.library.ualberta.ca/files/r494vk17h


Washington State University

8. [No author]. Supervised Learning in Dynamic Streaming Graphs .

Degree: 2016, Washington State University

 With the emergence of networked data, graph classification has received considerable interest during the past years. Most approaches to graph classification focus on designing effective… (more)

Subjects/Keywords: Computer science; Artificial intelligence; Dynamic graph; Graph classification; Graph kernel; Graph mining; Incremental learning

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APA (6th Edition):

author], [. (2016). Supervised Learning in Dynamic Streaming Graphs . (Thesis). Washington State University. Retrieved from http://hdl.handle.net/2376/12160

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Chicago Manual of Style (16th Edition):

author], [No. “Supervised Learning in Dynamic Streaming Graphs .” 2016. Thesis, Washington State University. Accessed August 07, 2020. http://hdl.handle.net/2376/12160.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

MLA Handbook (7th Edition):

author], [No. “Supervised Learning in Dynamic Streaming Graphs .” 2016. Web. 07 Aug 2020.

Vancouver:

author] [. Supervised Learning in Dynamic Streaming Graphs . [Internet] [Thesis]. Washington State University; 2016. [cited 2020 Aug 07]. Available from: http://hdl.handle.net/2376/12160.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Council of Science Editors:

author] [. Supervised Learning in Dynamic Streaming Graphs . [Thesis]. Washington State University; 2016. Available from: http://hdl.handle.net/2376/12160

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation


Louisiana State University

9. Shakya, Shobhit Sandesh. Pattern Mining and Events Discovery in Molecular Dynamics Simulations Data.

Degree: PhD, Computer Sciences, 2015, Louisiana State University

 Molecular dynamics simulation method is widely used to calculate and understand a wide range of properties of materials. A lot of research efforts have been… (more)

Subjects/Keywords: data mining; sub-graph mining; close path mining; molecular dynamics; pattern mining

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APA · Chicago · MLA · Vancouver · CSE | Export to Zotero / EndNote / Reference Manager

APA (6th Edition):

Shakya, S. S. (2015). Pattern Mining and Events Discovery in Molecular Dynamics Simulations Data. (Doctoral Dissertation). Louisiana State University. Retrieved from etd-04092015-224028 ; https://digitalcommons.lsu.edu/gradschool_dissertations/2034

Chicago Manual of Style (16th Edition):

Shakya, Shobhit Sandesh. “Pattern Mining and Events Discovery in Molecular Dynamics Simulations Data.” 2015. Doctoral Dissertation, Louisiana State University. Accessed August 07, 2020. etd-04092015-224028 ; https://digitalcommons.lsu.edu/gradschool_dissertations/2034.

MLA Handbook (7th Edition):

Shakya, Shobhit Sandesh. “Pattern Mining and Events Discovery in Molecular Dynamics Simulations Data.” 2015. Web. 07 Aug 2020.

Vancouver:

Shakya SS. Pattern Mining and Events Discovery in Molecular Dynamics Simulations Data. [Internet] [Doctoral dissertation]. Louisiana State University; 2015. [cited 2020 Aug 07]. Available from: etd-04092015-224028 ; https://digitalcommons.lsu.edu/gradschool_dissertations/2034.

Council of Science Editors:

Shakya SS. Pattern Mining and Events Discovery in Molecular Dynamics Simulations Data. [Doctoral Dissertation]. Louisiana State University; 2015. Available from: etd-04092015-224028 ; https://digitalcommons.lsu.edu/gradschool_dissertations/2034


Louisiana State University

10. Basuchowdhuri, Partha. Greedy methods for approximate graph matching with applications for social network analysis.

Degree: MSCS, Computer Sciences, 2009, Louisiana State University

 In this thesis, we study greedy algorithms for approximate sub-graph matching with attributed graphs. Such algorithms find one or multiple copies of a sub-graph pattern… (more)

Subjects/Keywords: Sub-Graph Mining; Seed; Social Network Analysis

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APA (6th Edition):

Basuchowdhuri, P. (2009). Greedy methods for approximate graph matching with applications for social network analysis. (Masters Thesis). Louisiana State University. Retrieved from etd-04132009-134102 ; https://digitalcommons.lsu.edu/gradschool_theses/3105

Chicago Manual of Style (16th Edition):

Basuchowdhuri, Partha. “Greedy methods for approximate graph matching with applications for social network analysis.” 2009. Masters Thesis, Louisiana State University. Accessed August 07, 2020. etd-04132009-134102 ; https://digitalcommons.lsu.edu/gradschool_theses/3105.

MLA Handbook (7th Edition):

Basuchowdhuri, Partha. “Greedy methods for approximate graph matching with applications for social network analysis.” 2009. Web. 07 Aug 2020.

Vancouver:

Basuchowdhuri P. Greedy methods for approximate graph matching with applications for social network analysis. [Internet] [Masters thesis]. Louisiana State University; 2009. [cited 2020 Aug 07]. Available from: etd-04132009-134102 ; https://digitalcommons.lsu.edu/gradschool_theses/3105.

Council of Science Editors:

Basuchowdhuri P. Greedy methods for approximate graph matching with applications for social network analysis. [Masters Thesis]. Louisiana State University; 2009. Available from: etd-04132009-134102 ; https://digitalcommons.lsu.edu/gradschool_theses/3105


Indiana University

11. Gao, Zheng. COMMUNITY DETECTION IN GRAPHS .

Degree: 2020, Indiana University

 Community detection has always been one of the fundamental research topics in graph mining. As a type of unsupervised or semi-supervised approach, community detection aims… (more)

Subjects/Keywords: community detection; complex network analysis; graph mining

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APA (6th Edition):

Gao, Z. (2020). COMMUNITY DETECTION IN GRAPHS . (Thesis). Indiana University. Retrieved from http://hdl.handle.net/2022/25623

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Chicago Manual of Style (16th Edition):

Gao, Zheng. “COMMUNITY DETECTION IN GRAPHS .” 2020. Thesis, Indiana University. Accessed August 07, 2020. http://hdl.handle.net/2022/25623.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

MLA Handbook (7th Edition):

Gao, Zheng. “COMMUNITY DETECTION IN GRAPHS .” 2020. Web. 07 Aug 2020.

Vancouver:

Gao Z. COMMUNITY DETECTION IN GRAPHS . [Internet] [Thesis]. Indiana University; 2020. [cited 2020 Aug 07]. Available from: http://hdl.handle.net/2022/25623.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Council of Science Editors:

Gao Z. COMMUNITY DETECTION IN GRAPHS . [Thesis]. Indiana University; 2020. Available from: http://hdl.handle.net/2022/25623

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

12. Zhao, Yuchen. Mining Large Graphs.

Degree: 2013, University of Illinois – Chicago

 Recently, there is an increasing need for mining graphs with the rapidly growing social networks, Internet applications and communication networks. Among all these real-world applications,… (more)

Subjects/Keywords: graph mining

…knowledge from graph structures to a number of fundamental data mining tasks. The graph structured… …mining techniques, the graph structures are complex and lack of existing features in the graph… …flows, etc. There are increasing needs for building models to mining graph data. In social… …cancers or chronic diseases (41; 84). Motivated by these challenges, graph mining has… …mining problems by studying graph structures. We first study using graph structures to… 

Page 1 Page 2 Page 3 Page 4 Page 5 Page 6 Page 7

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APA · Chicago · MLA · Vancouver · CSE | Export to Zotero / EndNote / Reference Manager

APA (6th Edition):

Zhao, Y. (2013). Mining Large Graphs. (Thesis). University of Illinois – Chicago. Retrieved from http://hdl.handle.net/10027/9955

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Chicago Manual of Style (16th Edition):

Zhao, Yuchen. “Mining Large Graphs.” 2013. Thesis, University of Illinois – Chicago. Accessed August 07, 2020. http://hdl.handle.net/10027/9955.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

MLA Handbook (7th Edition):

Zhao, Yuchen. “Mining Large Graphs.” 2013. Web. 07 Aug 2020.

Vancouver:

Zhao Y. Mining Large Graphs. [Internet] [Thesis]. University of Illinois – Chicago; 2013. [cited 2020 Aug 07]. Available from: http://hdl.handle.net/10027/9955.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Council of Science Editors:

Zhao Y. Mining Large Graphs. [Thesis]. University of Illinois – Chicago; 2013. Available from: http://hdl.handle.net/10027/9955

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation


University of Victoria

13. Daneshmandmehrabani, Mahsa. Truss decomposition in large probabilistic graphs.

Degree: Department of Computer Science, 2019, University of Victoria

 Truss decomposition is an essential problem in graph mining, which focuses on discovering dense subgraphs of a graph. Detecting trusses in deterministic graphs is extensively… (more)

Subjects/Keywords: Truss decomposition; graph mining; deterministic graphs

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APA (6th Edition):

Daneshmandmehrabani, M. (2019). Truss decomposition in large probabilistic graphs. (Masters Thesis). University of Victoria. Retrieved from http://hdl.handle.net/1828/11428

Chicago Manual of Style (16th Edition):

Daneshmandmehrabani, Mahsa. “Truss decomposition in large probabilistic graphs.” 2019. Masters Thesis, University of Victoria. Accessed August 07, 2020. http://hdl.handle.net/1828/11428.

MLA Handbook (7th Edition):

Daneshmandmehrabani, Mahsa. “Truss decomposition in large probabilistic graphs.” 2019. Web. 07 Aug 2020.

Vancouver:

Daneshmandmehrabani M. Truss decomposition in large probabilistic graphs. [Internet] [Masters thesis]. University of Victoria; 2019. [cited 2020 Aug 07]. Available from: http://hdl.handle.net/1828/11428.

Council of Science Editors:

Daneshmandmehrabani M. Truss decomposition in large probabilistic graphs. [Masters Thesis]. University of Victoria; 2019. Available from: http://hdl.handle.net/1828/11428


Virginia Tech

14. Cadena, Jose Eduardo. Finding Interesting Subgraphs with Guarantees.

Degree: PhD, Computer Science, 2018, Virginia Tech

 Networks are a mathematical abstraction of the interactions between a set of entities, with extensive applications in social science, epidemiology, bioinformatics, and cybersecurity, among others.… (more)

Subjects/Keywords: Graph Mining; Data Mining; Graph Algorithms; Anomaly Detection; Finding Subgraphs; Parameterized Complexity; Distributed Algorithms

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APA (6th Edition):

Cadena, J. E. (2018). Finding Interesting Subgraphs with Guarantees. (Doctoral Dissertation). Virginia Tech. Retrieved from http://hdl.handle.net/10919/81960

Chicago Manual of Style (16th Edition):

Cadena, Jose Eduardo. “Finding Interesting Subgraphs with Guarantees.” 2018. Doctoral Dissertation, Virginia Tech. Accessed August 07, 2020. http://hdl.handle.net/10919/81960.

MLA Handbook (7th Edition):

Cadena, Jose Eduardo. “Finding Interesting Subgraphs with Guarantees.” 2018. Web. 07 Aug 2020.

Vancouver:

Cadena JE. Finding Interesting Subgraphs with Guarantees. [Internet] [Doctoral dissertation]. Virginia Tech; 2018. [cited 2020 Aug 07]. Available from: http://hdl.handle.net/10919/81960.

Council of Science Editors:

Cadena JE. Finding Interesting Subgraphs with Guarantees. [Doctoral Dissertation]. Virginia Tech; 2018. Available from: http://hdl.handle.net/10919/81960


Iowa State University

15. Wang, Heyong. A two-stage strategy for solving the connection subgraph problem.

Degree: 2012, Iowa State University

 A connection subgraph is a small subgraph of a large graph that best capture the relationship between two nodes. Formally, Connection Subgraph Problem is: Given:… (more)

Subjects/Keywords: Connection Subgraph; Data Mining; Graph Betweenness; Graph Mining; Path Between; Computer Sciences

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APA (6th Edition):

Wang, H. (2012). A two-stage strategy for solving the connection subgraph problem. (Thesis). Iowa State University. Retrieved from https://lib.dr.iastate.edu/etd/12507

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Chicago Manual of Style (16th Edition):

Wang, Heyong. “A two-stage strategy for solving the connection subgraph problem.” 2012. Thesis, Iowa State University. Accessed August 07, 2020. https://lib.dr.iastate.edu/etd/12507.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

MLA Handbook (7th Edition):

Wang, Heyong. “A two-stage strategy for solving the connection subgraph problem.” 2012. Web. 07 Aug 2020.

Vancouver:

Wang H. A two-stage strategy for solving the connection subgraph problem. [Internet] [Thesis]. Iowa State University; 2012. [cited 2020 Aug 07]. Available from: https://lib.dr.iastate.edu/etd/12507.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Council of Science Editors:

Wang H. A two-stage strategy for solving the connection subgraph problem. [Thesis]. Iowa State University; 2012. Available from: https://lib.dr.iastate.edu/etd/12507

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation


Penn State University

16. Slota, George Michael. Irregular Graph Algorithms on Modern Multicore, Manycore, and Distributed Processing Systems.

Degree: PhD, Computer Science and Engineering, 2016, Penn State University

Graph analysis is the study of real-world interaction data, be it through biological or chemical interaction networks, human social or communication networks, or other graph-representable… (more)

Subjects/Keywords: graph mining; high performance computing; graph partitioning; subgraph isomorphism

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APA (6th Edition):

Slota, G. M. (2016). Irregular Graph Algorithms on Modern Multicore, Manycore, and Distributed Processing Systems. (Doctoral Dissertation). Penn State University. Retrieved from https://etda.libraries.psu.edu/catalog/28925

Chicago Manual of Style (16th Edition):

Slota, George Michael. “Irregular Graph Algorithms on Modern Multicore, Manycore, and Distributed Processing Systems.” 2016. Doctoral Dissertation, Penn State University. Accessed August 07, 2020. https://etda.libraries.psu.edu/catalog/28925.

MLA Handbook (7th Edition):

Slota, George Michael. “Irregular Graph Algorithms on Modern Multicore, Manycore, and Distributed Processing Systems.” 2016. Web. 07 Aug 2020.

Vancouver:

Slota GM. Irregular Graph Algorithms on Modern Multicore, Manycore, and Distributed Processing Systems. [Internet] [Doctoral dissertation]. Penn State University; 2016. [cited 2020 Aug 07]. Available from: https://etda.libraries.psu.edu/catalog/28925.

Council of Science Editors:

Slota GM. Irregular Graph Algorithms on Modern Multicore, Manycore, and Distributed Processing Systems. [Doctoral Dissertation]. Penn State University; 2016. Available from: https://etda.libraries.psu.edu/catalog/28925


Penn State University

17. Chatterjee, Anirban. Exploiting Sparsity, Structure, and Geometry for Knowledge Discovery.

Degree: PhD, Computer Science and Engineering, 2011, Penn State University

 Data-driven discovery seeks to obtain a computational model of the underlying process using observed data on a large number of variables. Observations can be viewed… (more)

Subjects/Keywords: sparse graph embedding; sparse graph partitioning; data mining; sparse linear solvers

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APA · Chicago · MLA · Vancouver · CSE | Export to Zotero / EndNote / Reference Manager

APA (6th Edition):

Chatterjee, A. (2011). Exploiting Sparsity, Structure, and Geometry for Knowledge Discovery. (Doctoral Dissertation). Penn State University. Retrieved from https://etda.libraries.psu.edu/catalog/12026

Chicago Manual of Style (16th Edition):

Chatterjee, Anirban. “Exploiting Sparsity, Structure, and Geometry for Knowledge Discovery.” 2011. Doctoral Dissertation, Penn State University. Accessed August 07, 2020. https://etda.libraries.psu.edu/catalog/12026.

MLA Handbook (7th Edition):

Chatterjee, Anirban. “Exploiting Sparsity, Structure, and Geometry for Knowledge Discovery.” 2011. Web. 07 Aug 2020.

Vancouver:

Chatterjee A. Exploiting Sparsity, Structure, and Geometry for Knowledge Discovery. [Internet] [Doctoral dissertation]. Penn State University; 2011. [cited 2020 Aug 07]. Available from: https://etda.libraries.psu.edu/catalog/12026.

Council of Science Editors:

Chatterjee A. Exploiting Sparsity, Structure, and Geometry for Knowledge Discovery. [Doctoral Dissertation]. Penn State University; 2011. Available from: https://etda.libraries.psu.edu/catalog/12026


Queensland University of Technology

18. Hassanzadeh, Reza. Anomaly detection in online social networks : using data-mining techniques and fuzzy logic.

Degree: 2014, Queensland University of Technology

 This research is a step forward in improving the accuracy of detecting anomaly in a data graph representing connectivity between people in an online social… (more)

Subjects/Keywords: Anomaly Detection; Fuzzy Logig; Data Mining; Data Graph; Graph Theory

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APA (6th Edition):

Hassanzadeh, R. (2014). Anomaly detection in online social networks : using data-mining techniques and fuzzy logic. (Thesis). Queensland University of Technology. Retrieved from https://eprints.qut.edu.au/78679/

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Chicago Manual of Style (16th Edition):

Hassanzadeh, Reza. “Anomaly detection in online social networks : using data-mining techniques and fuzzy logic.” 2014. Thesis, Queensland University of Technology. Accessed August 07, 2020. https://eprints.qut.edu.au/78679/.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

MLA Handbook (7th Edition):

Hassanzadeh, Reza. “Anomaly detection in online social networks : using data-mining techniques and fuzzy logic.” 2014. Web. 07 Aug 2020.

Vancouver:

Hassanzadeh R. Anomaly detection in online social networks : using data-mining techniques and fuzzy logic. [Internet] [Thesis]. Queensland University of Technology; 2014. [cited 2020 Aug 07]. Available from: https://eprints.qut.edu.au/78679/.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Council of Science Editors:

Hassanzadeh R. Anomaly detection in online social networks : using data-mining techniques and fuzzy logic. [Thesis]. Queensland University of Technology; 2014. Available from: https://eprints.qut.edu.au/78679/

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation


Virginia Tech

19. Maxwell, Evan Kyle. Graph Mining Algorithms for Memory Leak Diagnosis and Biological Database Clustering.

Degree: MS, Computer Science, 2010, Virginia Tech

 Large graph-based datasets are common to many applications because of the additional structure provided to data by graphs. Patterns extracted from graphs must adhere to… (more)

Subjects/Keywords: graph mining; graph clustering; multipartite cliques; memory leak detection; bioinformatics

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APA · Chicago · MLA · Vancouver · CSE | Export to Zotero / EndNote / Reference Manager

APA (6th Edition):

Maxwell, E. K. (2010). Graph Mining Algorithms for Memory Leak Diagnosis and Biological Database Clustering. (Masters Thesis). Virginia Tech. Retrieved from http://hdl.handle.net/10919/34008

Chicago Manual of Style (16th Edition):

Maxwell, Evan Kyle. “Graph Mining Algorithms for Memory Leak Diagnosis and Biological Database Clustering.” 2010. Masters Thesis, Virginia Tech. Accessed August 07, 2020. http://hdl.handle.net/10919/34008.

MLA Handbook (7th Edition):

Maxwell, Evan Kyle. “Graph Mining Algorithms for Memory Leak Diagnosis and Biological Database Clustering.” 2010. Web. 07 Aug 2020.

Vancouver:

Maxwell EK. Graph Mining Algorithms for Memory Leak Diagnosis and Biological Database Clustering. [Internet] [Masters thesis]. Virginia Tech; 2010. [cited 2020 Aug 07]. Available from: http://hdl.handle.net/10919/34008.

Council of Science Editors:

Maxwell EK. Graph Mining Algorithms for Memory Leak Diagnosis and Biological Database Clustering. [Masters Thesis]. Virginia Tech; 2010. Available from: http://hdl.handle.net/10919/34008


University of Melbourne

20. Rashidi, Lida. Anomaly detection in large evolving graphs.

Degree: 2017, University of Melbourne

 Anomaly detection plays a vital role in various application domains including network intrusion detection, environmental monitoring and road traffic analysis. However a major challenge in… (more)

Subjects/Keywords: anomaly detection; graph mining; matrix permutation; graph embedding; dynamic graphs

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APA (6th Edition):

Rashidi, L. (2017). Anomaly detection in large evolving graphs. (Doctoral Dissertation). University of Melbourne. Retrieved from http://hdl.handle.net/11343/194243

Chicago Manual of Style (16th Edition):

Rashidi, Lida. “Anomaly detection in large evolving graphs.” 2017. Doctoral Dissertation, University of Melbourne. Accessed August 07, 2020. http://hdl.handle.net/11343/194243.

MLA Handbook (7th Edition):

Rashidi, Lida. “Anomaly detection in large evolving graphs.” 2017. Web. 07 Aug 2020.

Vancouver:

Rashidi L. Anomaly detection in large evolving graphs. [Internet] [Doctoral dissertation]. University of Melbourne; 2017. [cited 2020 Aug 07]. Available from: http://hdl.handle.net/11343/194243.

Council of Science Editors:

Rashidi L. Anomaly detection in large evolving graphs. [Doctoral Dissertation]. University of Melbourne; 2017. Available from: http://hdl.handle.net/11343/194243


Arizona State University

21. Peng, Ruiyue. TiCTak: Target-Specific Centrality Manipulation on Large Networks.

Degree: Computer Science, 2016, Arizona State University

 Measuring node centrality is a critical common denominator behind many important graph mining tasks. While the existing literature offers a wealth of different node centrality… (more)

Subjects/Keywords: Computer science; graph connectivity optimization; graph mining; large networks; node centrality

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APA (6th Edition):

Peng, R. (2016). TiCTak: Target-Specific Centrality Manipulation on Large Networks. (Masters Thesis). Arizona State University. Retrieved from http://repository.asu.edu/items/40757

Chicago Manual of Style (16th Edition):

Peng, Ruiyue. “TiCTak: Target-Specific Centrality Manipulation on Large Networks.” 2016. Masters Thesis, Arizona State University. Accessed August 07, 2020. http://repository.asu.edu/items/40757.

MLA Handbook (7th Edition):

Peng, Ruiyue. “TiCTak: Target-Specific Centrality Manipulation on Large Networks.” 2016. Web. 07 Aug 2020.

Vancouver:

Peng R. TiCTak: Target-Specific Centrality Manipulation on Large Networks. [Internet] [Masters thesis]. Arizona State University; 2016. [cited 2020 Aug 07]. Available from: http://repository.asu.edu/items/40757.

Council of Science Editors:

Peng R. TiCTak: Target-Specific Centrality Manipulation on Large Networks. [Masters Thesis]. Arizona State University; 2016. Available from: http://repository.asu.edu/items/40757


Case Western Reserve University

22. Cederquist, Aaron. Frequent Pattern Mining among Weighted and Directed Graphs.

Degree: MSs, EECS - Computer and Information Sciences, 2009, Case Western Reserve University

Mining frequent graph patterns has great practical implications, since data in numerous application domains such as biology, sociology, and finance, can be represented as graphs.… (more)

Subjects/Keywords: Computer Science; data mining; graph; canonical form; frequent pattern mining

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APA · Chicago · MLA · Vancouver · CSE | Export to Zotero / EndNote / Reference Manager

APA (6th Edition):

Cederquist, A. (2009). Frequent Pattern Mining among Weighted and Directed Graphs. (Masters Thesis). Case Western Reserve University. Retrieved from http://rave.ohiolink.edu/etdc/view?acc_num=case1228328123

Chicago Manual of Style (16th Edition):

Cederquist, Aaron. “Frequent Pattern Mining among Weighted and Directed Graphs.” 2009. Masters Thesis, Case Western Reserve University. Accessed August 07, 2020. http://rave.ohiolink.edu/etdc/view?acc_num=case1228328123.

MLA Handbook (7th Edition):

Cederquist, Aaron. “Frequent Pattern Mining among Weighted and Directed Graphs.” 2009. Web. 07 Aug 2020.

Vancouver:

Cederquist A. Frequent Pattern Mining among Weighted and Directed Graphs. [Internet] [Masters thesis]. Case Western Reserve University; 2009. [cited 2020 Aug 07]. Available from: http://rave.ohiolink.edu/etdc/view?acc_num=case1228328123.

Council of Science Editors:

Cederquist A. Frequent Pattern Mining among Weighted and Directed Graphs. [Masters Thesis]. Case Western Reserve University; 2009. Available from: http://rave.ohiolink.edu/etdc/view?acc_num=case1228328123


Texas A&M University

23. Sangelkar, Shraddha Chandrakant. Automated Inclusive Design Heuristics Generation with Graph Mining.

Degree: 2013, Texas A&M University

 Inclusive design is a concept intended to promote the development of products and environments equally usable by all users, irrespective of their age or ability.… (more)

Subjects/Keywords: Inclusive Design; Graph Mining; Data Mining; User Centric Design; Universal Design

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APA (6th Edition):

Sangelkar, S. C. (2013). Automated Inclusive Design Heuristics Generation with Graph Mining. (Thesis). Texas A&M University. Retrieved from http://hdl.handle.net/1969.1/151329

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Chicago Manual of Style (16th Edition):

Sangelkar, Shraddha Chandrakant. “Automated Inclusive Design Heuristics Generation with Graph Mining.” 2013. Thesis, Texas A&M University. Accessed August 07, 2020. http://hdl.handle.net/1969.1/151329.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

MLA Handbook (7th Edition):

Sangelkar, Shraddha Chandrakant. “Automated Inclusive Design Heuristics Generation with Graph Mining.” 2013. Web. 07 Aug 2020.

Vancouver:

Sangelkar SC. Automated Inclusive Design Heuristics Generation with Graph Mining. [Internet] [Thesis]. Texas A&M University; 2013. [cited 2020 Aug 07]. Available from: http://hdl.handle.net/1969.1/151329.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Council of Science Editors:

Sangelkar SC. Automated Inclusive Design Heuristics Generation with Graph Mining. [Thesis]. Texas A&M University; 2013. Available from: http://hdl.handle.net/1969.1/151329

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

24. Voudigari, Elli. Graph mining In complex networks and prediction in the web graph.

Degree: 2018, Athens University Economics and Business (AUEB); Οικονομικό Πανεπιστήμιο Αθηνών

 The study of real world networks has attracted considerable attention over the last decades from many disciplines including biology, computer science, economics, engineering, mathematics, physics,… (more)

Subjects/Keywords: Εξόρυξη γνώσης από γράφους; Graph mining; Web mining

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APA (6th Edition):

Voudigari, E. (2018). Graph mining In complex networks and prediction in the web graph. (Thesis). Athens University Economics and Business (AUEB); Οικονομικό Πανεπιστήμιο Αθηνών. Retrieved from http://hdl.handle.net/10442/hedi/44611

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Chicago Manual of Style (16th Edition):

Voudigari, Elli. “Graph mining In complex networks and prediction in the web graph.” 2018. Thesis, Athens University Economics and Business (AUEB); Οικονομικό Πανεπιστήμιο Αθηνών. Accessed August 07, 2020. http://hdl.handle.net/10442/hedi/44611.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

MLA Handbook (7th Edition):

Voudigari, Elli. “Graph mining In complex networks and prediction in the web graph.” 2018. Web. 07 Aug 2020.

Vancouver:

Voudigari E. Graph mining In complex networks and prediction in the web graph. [Internet] [Thesis]. Athens University Economics and Business (AUEB); Οικονομικό Πανεπιστήμιο Αθηνών; 2018. [cited 2020 Aug 07]. Available from: http://hdl.handle.net/10442/hedi/44611.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Council of Science Editors:

Voudigari E. Graph mining In complex networks and prediction in the web graph. [Thesis]. Athens University Economics and Business (AUEB); Οικονομικό Πανεπιστήμιο Αθηνών; 2018. Available from: http://hdl.handle.net/10442/hedi/44611

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation


Université Catholique de Louvain

25. Senelle, Mathieu. Measures on graphs : from similarity to density.

Degree: 2014, Université Catholique de Louvain

In this thesis, we mainly consider two closely related classical and general problems in data mining and machine learning: (1) developing new similarity measures between… (more)

Subjects/Keywords: Data Mining; Graph Mining; Classification; Clustering; Matrix-Forest Theorem

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APA (6th Edition):

Senelle, M. (2014). Measures on graphs : from similarity to density. (Thesis). Université Catholique de Louvain. Retrieved from http://hdl.handle.net/2078.1/161671

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Chicago Manual of Style (16th Edition):

Senelle, Mathieu. “Measures on graphs : from similarity to density.” 2014. Thesis, Université Catholique de Louvain. Accessed August 07, 2020. http://hdl.handle.net/2078.1/161671.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

MLA Handbook (7th Edition):

Senelle, Mathieu. “Measures on graphs : from similarity to density.” 2014. Web. 07 Aug 2020.

Vancouver:

Senelle M. Measures on graphs : from similarity to density. [Internet] [Thesis]. Université Catholique de Louvain; 2014. [cited 2020 Aug 07]. Available from: http://hdl.handle.net/2078.1/161671.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Council of Science Editors:

Senelle M. Measures on graphs : from similarity to density. [Thesis]. Université Catholique de Louvain; 2014. Available from: http://hdl.handle.net/2078.1/161671

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation


Iowa State University

26. Mousavi Hanjani, Kiana. Improved triangle counting in graph streams: Neighborhood multi-sampling.

Degree: 2018, Iowa State University

 In this thesis, we study the problem of estimating the number of triangles of an undirected graph in the data stream model. Some of the… (more)

Subjects/Keywords: big data; data mining; graph mining; streaming algorithms; Computer Sciences

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APA (6th Edition):

Mousavi Hanjani, K. (2018). Improved triangle counting in graph streams: Neighborhood multi-sampling. (Thesis). Iowa State University. Retrieved from https://lib.dr.iastate.edu/etd/16644

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Chicago Manual of Style (16th Edition):

Mousavi Hanjani, Kiana. “Improved triangle counting in graph streams: Neighborhood multi-sampling.” 2018. Thesis, Iowa State University. Accessed August 07, 2020. https://lib.dr.iastate.edu/etd/16644.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

MLA Handbook (7th Edition):

Mousavi Hanjani, Kiana. “Improved triangle counting in graph streams: Neighborhood multi-sampling.” 2018. Web. 07 Aug 2020.

Vancouver:

Mousavi Hanjani K. Improved triangle counting in graph streams: Neighborhood multi-sampling. [Internet] [Thesis]. Iowa State University; 2018. [cited 2020 Aug 07]. Available from: https://lib.dr.iastate.edu/etd/16644.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Council of Science Editors:

Mousavi Hanjani K. Improved triangle counting in graph streams: Neighborhood multi-sampling. [Thesis]. Iowa State University; 2018. Available from: https://lib.dr.iastate.edu/etd/16644

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation


University of Oregon

27. Liu, Haishan. A Graph-based Approach for Semantic Data Mining.

Degree: 2012, University of Oregon

 Data mining is the nontrivial extraction of implicit, previously unknown, and potentially useful information from data. It is widely acknowledged that the role of domain… (more)

Subjects/Keywords: Domain knowledge; Graph mining; Ontology; Semantic data mining

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APA (6th Edition):

Liu, H. (2012). A Graph-based Approach for Semantic Data Mining. (Thesis). University of Oregon. Retrieved from http://hdl.handle.net/1794/12567

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Chicago Manual of Style (16th Edition):

Liu, Haishan. “A Graph-based Approach for Semantic Data Mining.” 2012. Thesis, University of Oregon. Accessed August 07, 2020. http://hdl.handle.net/1794/12567.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

MLA Handbook (7th Edition):

Liu, Haishan. “A Graph-based Approach for Semantic Data Mining.” 2012. Web. 07 Aug 2020.

Vancouver:

Liu H. A Graph-based Approach for Semantic Data Mining. [Internet] [Thesis]. University of Oregon; 2012. [cited 2020 Aug 07]. Available from: http://hdl.handle.net/1794/12567.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Council of Science Editors:

Liu H. A Graph-based Approach for Semantic Data Mining. [Thesis]. University of Oregon; 2012. Available from: http://hdl.handle.net/1794/12567

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation


Clemson University

28. Qiu, Zirou. ELRUNA: Elimination Rule-based Network Alignment.

Degree: MS, School of Computing, 2020, Clemson University

  Networks model a variety of complex phenomena across different domains. In many applications, one of the most essential tasks is to align two or… (more)

Subjects/Keywords: combinatorial optimization; data mining; graph mining; network alignment

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APA (6th Edition):

Qiu, Z. (2020). ELRUNA: Elimination Rule-based Network Alignment. (Masters Thesis). Clemson University. Retrieved from https://tigerprints.clemson.edu/all_theses/3271

Chicago Manual of Style (16th Edition):

Qiu, Zirou. “ELRUNA: Elimination Rule-based Network Alignment.” 2020. Masters Thesis, Clemson University. Accessed August 07, 2020. https://tigerprints.clemson.edu/all_theses/3271.

MLA Handbook (7th Edition):

Qiu, Zirou. “ELRUNA: Elimination Rule-based Network Alignment.” 2020. Web. 07 Aug 2020.

Vancouver:

Qiu Z. ELRUNA: Elimination Rule-based Network Alignment. [Internet] [Masters thesis]. Clemson University; 2020. [cited 2020 Aug 07]. Available from: https://tigerprints.clemson.edu/all_theses/3271.

Council of Science Editors:

Qiu Z. ELRUNA: Elimination Rule-based Network Alignment. [Masters Thesis]. Clemson University; 2020. Available from: https://tigerprints.clemson.edu/all_theses/3271


Purdue University

29. Ahmed, Nesreen Kamel. Scaling Up Network Analysis and Mining: Statistical Sampling, Estimation, and Pattern Discovery.

Degree: PhD, Computer Science, 2015, Purdue University

 Network analysis and graph mining play a prominent role in providing insights and studying phenomena across various domains, including social, behavioral, biological, transportation, communication, and… (more)

Subjects/Keywords: Data Mining; Graph Mining; Machine Learning; Network Science; Sampling; Statistical Estimation

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APA (6th Edition):

Ahmed, N. K. (2015). Scaling Up Network Analysis and Mining: Statistical Sampling, Estimation, and Pattern Discovery. (Doctoral Dissertation). Purdue University. Retrieved from https://docs.lib.purdue.edu/open_access_dissertations/1445

Chicago Manual of Style (16th Edition):

Ahmed, Nesreen Kamel. “Scaling Up Network Analysis and Mining: Statistical Sampling, Estimation, and Pattern Discovery.” 2015. Doctoral Dissertation, Purdue University. Accessed August 07, 2020. https://docs.lib.purdue.edu/open_access_dissertations/1445.

MLA Handbook (7th Edition):

Ahmed, Nesreen Kamel. “Scaling Up Network Analysis and Mining: Statistical Sampling, Estimation, and Pattern Discovery.” 2015. Web. 07 Aug 2020.

Vancouver:

Ahmed NK. Scaling Up Network Analysis and Mining: Statistical Sampling, Estimation, and Pattern Discovery. [Internet] [Doctoral dissertation]. Purdue University; 2015. [cited 2020 Aug 07]. Available from: https://docs.lib.purdue.edu/open_access_dissertations/1445.

Council of Science Editors:

Ahmed NK. Scaling Up Network Analysis and Mining: Statistical Sampling, Estimation, and Pattern Discovery. [Doctoral Dissertation]. Purdue University; 2015. Available from: https://docs.lib.purdue.edu/open_access_dissertations/1445


University of Melbourne

30. Yang, Meng. Video surveillance based crowd activity analysis.

Degree: 2019, University of Melbourne

 Video-based crowd motion analysis is an important problem in surveillance applications. Tasks such as identifying anomalous crowd motion patterns, finding sudden changes in the size… (more)

Subjects/Keywords: computer vision; data mining; graph mining; anomalous event detection; video surveillance

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APA · Chicago · MLA · Vancouver · CSE | Export to Zotero / EndNote / Reference Manager

APA (6th Edition):

Yang, M. (2019). Video surveillance based crowd activity analysis. (Doctoral Dissertation). University of Melbourne. Retrieved from http://hdl.handle.net/11343/230885

Chicago Manual of Style (16th Edition):

Yang, Meng. “Video surveillance based crowd activity analysis.” 2019. Doctoral Dissertation, University of Melbourne. Accessed August 07, 2020. http://hdl.handle.net/11343/230885.

MLA Handbook (7th Edition):

Yang, Meng. “Video surveillance based crowd activity analysis.” 2019. Web. 07 Aug 2020.

Vancouver:

Yang M. Video surveillance based crowd activity analysis. [Internet] [Doctoral dissertation]. University of Melbourne; 2019. [cited 2020 Aug 07]. Available from: http://hdl.handle.net/11343/230885.

Council of Science Editors:

Yang M. Video surveillance based crowd activity analysis. [Doctoral Dissertation]. University of Melbourne; 2019. Available from: http://hdl.handle.net/11343/230885

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