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Showing records 1 – 13 of
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Penn State University

1. Bomiriya, Rashmi Pankajai. Topics in Exponential Random Graph Modeling.

Degree: 2014, Penn State University

URL: https://submit-etda.libraries.psu.edu/catalog/22448

► Exponential-family Random Graph Models (ERGMs) are a class of models that is frequently used for modeling social *networks*. ERGMs allow structural features as well as…
(more)

Subjects/Keywords: ERGMs; networks; bipartite; homophily; semi-parametric Bayesian; infectious disease; SEIR

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APA (6^{th} Edition):

Bomiriya, R. P. (2014). Topics in Exponential Random Graph Modeling. (Thesis). Penn State University. Retrieved from https://submit-etda.libraries.psu.edu/catalog/22448

Note: this citation may be lacking information needed for this citation format:

Not specified: Masters Thesis or Doctoral Dissertation

Chicago Manual of Style (16^{th} Edition):

Bomiriya, Rashmi Pankajai. “Topics in Exponential Random Graph Modeling.” 2014. Thesis, Penn State University. Accessed October 25, 2020. https://submit-etda.libraries.psu.edu/catalog/22448.

Note: this citation may be lacking information needed for this citation format:

Not specified: Masters Thesis or Doctoral Dissertation

MLA Handbook (7^{th} Edition):

Bomiriya, Rashmi Pankajai. “Topics in Exponential Random Graph Modeling.” 2014. Web. 25 Oct 2020.

Vancouver:

Bomiriya RP. Topics in Exponential Random Graph Modeling. [Internet] [Thesis]. Penn State University; 2014. [cited 2020 Oct 25]. Available from: https://submit-etda.libraries.psu.edu/catalog/22448.

Note: this citation may be lacking information needed for this citation format:

Not specified: Masters Thesis or Doctoral Dissertation

Council of Science Editors:

Bomiriya RP. Topics in Exponential Random Graph Modeling. [Thesis]. Penn State University; 2014. Available from: https://submit-etda.libraries.psu.edu/catalog/22448

Not specified: Masters Thesis or Doctoral Dissertation

RMIT University

2.
Liebig, J.
Identifying significant behaviour in complex *bipartite* * networks*.

Degree: 2016, RMIT University

URL: http://researchbank.rmit.edu.au/view/rmit:161937

► The study of complex *networks* has received much attention over the past few decades, presenting a simple, yet efficient means of modelling and understanding complex…
(more)

Subjects/Keywords: Fields of Research; Bipartite networks; Significant behaviour; One-mode projection; Backbone extraction; Clustering coefficient

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APA (6^{th} Edition):

Liebig, J. (2016). Identifying significant behaviour in complex bipartite networks. (Thesis). RMIT University. Retrieved from http://researchbank.rmit.edu.au/view/rmit:161937

Not specified: Masters Thesis or Doctoral Dissertation

Chicago Manual of Style (16^{th} Edition):

Liebig, J. “Identifying significant behaviour in complex bipartite networks.” 2016. Thesis, RMIT University. Accessed October 25, 2020. http://researchbank.rmit.edu.au/view/rmit:161937.

Not specified: Masters Thesis or Doctoral Dissertation

MLA Handbook (7^{th} Edition):

Liebig, J. “Identifying significant behaviour in complex bipartite networks.” 2016. Web. 25 Oct 2020.

Vancouver:

Liebig J. Identifying significant behaviour in complex bipartite networks. [Internet] [Thesis]. RMIT University; 2016. [cited 2020 Oct 25]. Available from: http://researchbank.rmit.edu.au/view/rmit:161937.

Not specified: Masters Thesis or Doctoral Dissertation

Council of Science Editors:

Liebig J. Identifying significant behaviour in complex bipartite networks. [Thesis]. RMIT University; 2016. Available from: http://researchbank.rmit.edu.au/view/rmit:161937

Not specified: Masters Thesis or Doctoral Dissertation

University of Washington

3.
Harris, Kameron Decker.
This Brain Is a Mess: Inference, Random Graphs, and Biophysics to Disentangle Neuronal * Networks*.

Degree: PhD, 2018, University of Washington

URL: http://hdl.handle.net/1773/40831

► At first glance, the neuronal network seems like a tangled web in many areas throughout the nervous system. Often, our best guess is that such…
(more)

Subjects/Keywords: Bipartite; Graph theory; Inference; Networks; Neuroscience; Rhythms; Mathematics; Neurosciences; Statistics; Applied mathematics

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APA (6^{th} Edition):

Harris, K. D. (2018). This Brain Is a Mess: Inference, Random Graphs, and Biophysics to Disentangle Neuronal Networks. (Doctoral Dissertation). University of Washington. Retrieved from http://hdl.handle.net/1773/40831

Chicago Manual of Style (16^{th} Edition):

Harris, Kameron Decker. “This Brain Is a Mess: Inference, Random Graphs, and Biophysics to Disentangle Neuronal Networks.” 2018. Doctoral Dissertation, University of Washington. Accessed October 25, 2020. http://hdl.handle.net/1773/40831.

MLA Handbook (7^{th} Edition):

Harris, Kameron Decker. “This Brain Is a Mess: Inference, Random Graphs, and Biophysics to Disentangle Neuronal Networks.” 2018. Web. 25 Oct 2020.

Vancouver:

Harris KD. This Brain Is a Mess: Inference, Random Graphs, and Biophysics to Disentangle Neuronal Networks. [Internet] [Doctoral dissertation]. University of Washington; 2018. [cited 2020 Oct 25]. Available from: http://hdl.handle.net/1773/40831.

Council of Science Editors:

Harris KD. This Brain Is a Mess: Inference, Random Graphs, and Biophysics to Disentangle Neuronal Networks. [Doctoral Dissertation]. University of Washington; 2018. Available from: http://hdl.handle.net/1773/40831

University of Toronto

4. Watts, Alexander Gordon. Effects of Landscape Spatial Heterogeneity on Host-Parasite Ecology.

Degree: PhD, 2015, University of Toronto

URL: http://hdl.handle.net/1807/71415

► Landscape spatial heterogeneity interacts with ecological processes that influence pathogen emergence and infectious disease spread. Modification of landscape composition and configuration is hypothesized to alter…
(more)

Subjects/Keywords: bipartite networks; disease ecology; landscape connectivity; Lyme disease; spatial epidemiology; urban ecology; 0329

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APA (6^{th} Edition):

Watts, A. G. (2015). Effects of Landscape Spatial Heterogeneity on Host-Parasite Ecology. (Doctoral Dissertation). University of Toronto. Retrieved from http://hdl.handle.net/1807/71415

Chicago Manual of Style (16^{th} Edition):

Watts, Alexander Gordon. “Effects of Landscape Spatial Heterogeneity on Host-Parasite Ecology.” 2015. Doctoral Dissertation, University of Toronto. Accessed October 25, 2020. http://hdl.handle.net/1807/71415.

MLA Handbook (7^{th} Edition):

Watts, Alexander Gordon. “Effects of Landscape Spatial Heterogeneity on Host-Parasite Ecology.” 2015. Web. 25 Oct 2020.

Vancouver:

Watts AG. Effects of Landscape Spatial Heterogeneity on Host-Parasite Ecology. [Internet] [Doctoral dissertation]. University of Toronto; 2015. [cited 2020 Oct 25]. Available from: http://hdl.handle.net/1807/71415.

Council of Science Editors:

Watts AG. Effects of Landscape Spatial Heterogeneity on Host-Parasite Ecology. [Doctoral Dissertation]. University of Toronto; 2015. Available from: http://hdl.handle.net/1807/71415

RMIT University

5.
Alzahrani, T.
Complex information *networks* – detecting community structure in *bipartite* * networks*.

Degree: 2016, RMIT University

URL: http://researchbank.rmit.edu.au/view/rmit:161636

► The last decade has witnessed great expansion in research and study of complex *networks*. A complex network is a large-scale network that reflects the interactions…
(more)

Subjects/Keywords: Fields of Research; Complex networks; Community detection; Bipartite networks; Overlapping communities; Algorithm and complexity; Infomap algorithm

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APA (6^{th} Edition):

Alzahrani, T. (2016). Complex information networks – detecting community structure in bipartite networks. (Thesis). RMIT University. Retrieved from http://researchbank.rmit.edu.au/view/rmit:161636

Not specified: Masters Thesis or Doctoral Dissertation

Chicago Manual of Style (16^{th} Edition):

Alzahrani, T. “Complex information networks – detecting community structure in bipartite networks.” 2016. Thesis, RMIT University. Accessed October 25, 2020. http://researchbank.rmit.edu.au/view/rmit:161636.

Not specified: Masters Thesis or Doctoral Dissertation

MLA Handbook (7^{th} Edition):

Alzahrani, T. “Complex information networks – detecting community structure in bipartite networks.” 2016. Web. 25 Oct 2020.

Vancouver:

Alzahrani T. Complex information networks – detecting community structure in bipartite networks. [Internet] [Thesis]. RMIT University; 2016. [cited 2020 Oct 25]. Available from: http://researchbank.rmit.edu.au/view/rmit:161636.

Not specified: Masters Thesis or Doctoral Dissertation

Council of Science Editors:

Alzahrani T. Complex information networks – detecting community structure in bipartite networks. [Thesis]. RMIT University; 2016. Available from: http://researchbank.rmit.edu.au/view/rmit:161636

Not specified: Masters Thesis or Doctoral Dissertation

University of Houston

6.
Trevino, Santiago 1981-.
Detecting Communities in Complex Unipartite and *Bipartite* *Networks* by Maximizing the Modularity.

Degree: PhD, Physics, 2013, University of Houston

URL: http://hdl.handle.net/10657/1216

► This dissertation develops and improves methods to detect the modular structure of complex unipartite and *bipartite* *networks* using the method of modularity maximization, in which…
(more)

Subjects/Keywords: Community detection; Modularity; Complex networks; Networks; Clustering; Modules; Bipartite; Unipartite; Z-score; Genetics; Metabolism; E. coli

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APA (6^{th} Edition):

Trevino, S. 1. (2013). Detecting Communities in Complex Unipartite and Bipartite Networks by Maximizing the Modularity. (Doctoral Dissertation). University of Houston. Retrieved from http://hdl.handle.net/10657/1216

Chicago Manual of Style (16^{th} Edition):

Trevino, Santiago 1981-. “Detecting Communities in Complex Unipartite and Bipartite Networks by Maximizing the Modularity.” 2013. Doctoral Dissertation, University of Houston. Accessed October 25, 2020. http://hdl.handle.net/10657/1216.

MLA Handbook (7^{th} Edition):

Trevino, Santiago 1981-. “Detecting Communities in Complex Unipartite and Bipartite Networks by Maximizing the Modularity.” 2013. Web. 25 Oct 2020.

Vancouver:

Trevino S1. Detecting Communities in Complex Unipartite and Bipartite Networks by Maximizing the Modularity. [Internet] [Doctoral dissertation]. University of Houston; 2013. [cited 2020 Oct 25]. Available from: http://hdl.handle.net/10657/1216.

Council of Science Editors:

Trevino S1. Detecting Communities in Complex Unipartite and Bipartite Networks by Maximizing the Modularity. [Doctoral Dissertation]. University of Houston; 2013. Available from: http://hdl.handle.net/10657/1216

University of Arkansas

7.
Ashmore, Stephen Charles.
Evaluating the Intrinsic Similarity between Neural * Networks*.

Degree: MS, 2015, University of Arkansas

URL: https://scholarworks.uark.edu/etd/1395

► We present Forward *Bipartite* Alignment (FBA), a method that aligns the topological structures of two neural *networks*. Neural *networks* are considered to be a…
(more)

Subjects/Keywords: Applied sciences; Forward bipartite alignment; Machine learning; Neutral network; Artificial Intelligence and Robotics; OS and Networks

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APA (6^{th} Edition):

Ashmore, S. C. (2015). Evaluating the Intrinsic Similarity between Neural Networks. (Masters Thesis). University of Arkansas. Retrieved from https://scholarworks.uark.edu/etd/1395

Chicago Manual of Style (16^{th} Edition):

Ashmore, Stephen Charles. “Evaluating the Intrinsic Similarity between Neural Networks.” 2015. Masters Thesis, University of Arkansas. Accessed October 25, 2020. https://scholarworks.uark.edu/etd/1395.

MLA Handbook (7^{th} Edition):

Ashmore, Stephen Charles. “Evaluating the Intrinsic Similarity between Neural Networks.” 2015. Web. 25 Oct 2020.

Vancouver:

Ashmore SC. Evaluating the Intrinsic Similarity between Neural Networks. [Internet] [Masters thesis]. University of Arkansas; 2015. [cited 2020 Oct 25]. Available from: https://scholarworks.uark.edu/etd/1395.

Council of Science Editors:

Ashmore SC. Evaluating the Intrinsic Similarity between Neural Networks. [Masters Thesis]. University of Arkansas; 2015. Available from: https://scholarworks.uark.edu/etd/1395

8.
Flores Garcia, César O.
Phage – Bacteria Infection *networks*: from nestedness to modularity and back again.

Degree: PhD, Physics, 2014, Georgia Tech

URL: http://hdl.handle.net/1853/53007

► Bacteriophages (viruses that infect bacteria) are the most abundant biological life-forms on Earth. However, very little is known regarding the structure of phage-bacteria infections. In…
(more)

Subjects/Keywords: Bipartite graphs; Ecology; Phages; Bacteria; Interactions; Network theory; Complex networks; Bipartite networks; Computational ecology

…represented as
a *bipartite* network (see below). Researchers have shown that these *networks*… …case for plant–pollinator *networks*, this relationship
can be represented as a *bipartite*… …that *bipartite* *networks* can be described in reference to four general
*bipartite* network… …relevant questions of *bipartite* ecological *networks* that can be answered. In a
sense, we have to… …*Bipartite* ecological network . . . . . . . . . . . . . . . . . .
61
4.2.2
Algorithms…

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APA (6^{th} Edition):

Flores Garcia, C. O. (2014). Phage – Bacteria Infection networks: from nestedness to modularity and back again. (Doctoral Dissertation). Georgia Tech. Retrieved from http://hdl.handle.net/1853/53007

Chicago Manual of Style (16^{th} Edition):

Flores Garcia, César O. “Phage – Bacteria Infection networks: from nestedness to modularity and back again.” 2014. Doctoral Dissertation, Georgia Tech. Accessed October 25, 2020. http://hdl.handle.net/1853/53007.

MLA Handbook (7^{th} Edition):

Flores Garcia, César O. “Phage – Bacteria Infection networks: from nestedness to modularity and back again.” 2014. Web. 25 Oct 2020.

Vancouver:

Flores Garcia CO. Phage – Bacteria Infection networks: from nestedness to modularity and back again. [Internet] [Doctoral dissertation]. Georgia Tech; 2014. [cited 2020 Oct 25]. Available from: http://hdl.handle.net/1853/53007.

Council of Science Editors:

Flores Garcia CO. Phage – Bacteria Infection networks: from nestedness to modularity and back again. [Doctoral Dissertation]. Georgia Tech; 2014. Available from: http://hdl.handle.net/1853/53007

9.
[No author].
* Bipartite* Network Community Detection: Algorithms and Applications
.

Degree: 2018, Washington State University

URL: http://hdl.handle.net/2376/16377

► Methods to eﬃciently uncover and extract community structures are required in a vast number of applications where networked data and their interactions can be modeled…
(more)

Subjects/Keywords: Computer science; Bioinformatics; bipartite modularity; bipartite networks; clustering; community detection; graph algorithms; heterogeneous biological data

…BACKGROUND . . . . . . . . . . . . . . . . . .
9
2.1 *Bipartite* *Networks*… …biLouvain Experimental Results on *Bipartite* Plant-Pollinator *Networks* 133
A.1.1
Performance… …Results of the biLouvain algorithm on *bipartite* *networks* with homogeneous bicliques… …4.4
Results of the biLouvain algorithm on *bipartite* *networks* with heterogeneous bicliques… …in *bipartite* *networks*.
4
1.2 Communities detected by our biLouvain algorithm in the…

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

APA (6^{th} Edition):

author], [. (2018). Bipartite Network Community Detection: Algorithms and Applications . (Thesis). Washington State University. Retrieved from http://hdl.handle.net/2376/16377

Not specified: Masters Thesis or Doctoral Dissertation

Chicago Manual of Style (16^{th} Edition):

author], [No. “Bipartite Network Community Detection: Algorithms and Applications .” 2018. Thesis, Washington State University. Accessed October 25, 2020. http://hdl.handle.net/2376/16377.

Not specified: Masters Thesis or Doctoral Dissertation

MLA Handbook (7^{th} Edition):

author], [No. “Bipartite Network Community Detection: Algorithms and Applications .” 2018. Web. 25 Oct 2020.

Vancouver:

author] [. Bipartite Network Community Detection: Algorithms and Applications . [Internet] [Thesis]. Washington State University; 2018. [cited 2020 Oct 25]. Available from: http://hdl.handle.net/2376/16377.

Not specified: Masters Thesis or Doctoral Dissertation

Council of Science Editors:

author] [. Bipartite Network Community Detection: Algorithms and Applications . [Thesis]. Washington State University; 2018. Available from: http://hdl.handle.net/2376/16377

Not specified: Masters Thesis or Doctoral Dissertation

Harvard University

10.
Schlossberger, Janelle.
Mapping *Networks* to Probability Distributions in the Economy.

Degree: PhD, 2019, Harvard University

URL: http://nrs.harvard.edu/urn-3:HUL.InstRepos:42029663

►

This dissertation develops and applies a set of theoretical tools that allows us to explicitly map the topologies of *networks* in the economy to different…
(more)

Subjects/Keywords: network; probability distribution; economic system; configuration; uncertainty modeling; macroeconomic sentiment; animal spirits; voting; economic multiplier; stimulus; transfers; strategic complements and substitutes; coordination and anti-coordination; production networks; stress testing; global market shock scenarios; macroprudential versus microprudential regulation; bipartite networks

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APA (6^{th} Edition):

Schlossberger, J. (2019). Mapping Networks to Probability Distributions in the Economy. (Doctoral Dissertation). Harvard University. Retrieved from http://nrs.harvard.edu/urn-3:HUL.InstRepos:42029663

Chicago Manual of Style (16^{th} Edition):

Schlossberger, Janelle. “Mapping Networks to Probability Distributions in the Economy.” 2019. Doctoral Dissertation, Harvard University. Accessed October 25, 2020. http://nrs.harvard.edu/urn-3:HUL.InstRepos:42029663.

MLA Handbook (7^{th} Edition):

Schlossberger, Janelle. “Mapping Networks to Probability Distributions in the Economy.” 2019. Web. 25 Oct 2020.

Vancouver:

Schlossberger J. Mapping Networks to Probability Distributions in the Economy. [Internet] [Doctoral dissertation]. Harvard University; 2019. [cited 2020 Oct 25]. Available from: http://nrs.harvard.edu/urn-3:HUL.InstRepos:42029663.

Council of Science Editors:

Schlossberger J. Mapping Networks to Probability Distributions in the Economy. [Doctoral Dissertation]. Harvard University; 2019. Available from: http://nrs.harvard.edu/urn-3:HUL.InstRepos:42029663

11. Pires, Matheus Giovanni. Abordagem neuro-genética para mapeamento de problemas de conexão em otimização combinatória.

Degree: PhD, Sistemas Dinâmicos, 2009, University of São Paulo

URL: http://www.teses.usp.br/teses/disponiveis/18/18153/tde-12062009-150911/ ;

►

Devido a restrições de aplicabilidade presentes nos algoritmos para a solução de problemas de otimização combinatória, os sistemas baseados em redes neurais artificiais e algoritmos… (more)

Subjects/Keywords: Algoritmos genéticos; Artificial neural networks; Bipartite graph optimization; Combinatorial optimization; Genetic algorithms; N-Queens problem; Otimização combinatória; Problema das N-Rainhas; Problema do caminho mínimo; Problema do emparelhamento bipartido; Redes neurais artificiais; Shortest path problem

Record Details Similar Records

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APA (6^{th} Edition):

Pires, M. G. (2009). Abordagem neuro-genética para mapeamento de problemas de conexão em otimização combinatória. (Doctoral Dissertation). University of São Paulo. Retrieved from http://www.teses.usp.br/teses/disponiveis/18/18153/tde-12062009-150911/ ;

Chicago Manual of Style (16^{th} Edition):

Pires, Matheus Giovanni. “Abordagem neuro-genética para mapeamento de problemas de conexão em otimização combinatória.” 2009. Doctoral Dissertation, University of São Paulo. Accessed October 25, 2020. http://www.teses.usp.br/teses/disponiveis/18/18153/tde-12062009-150911/ ;.

MLA Handbook (7^{th} Edition):

Pires, Matheus Giovanni. “Abordagem neuro-genética para mapeamento de problemas de conexão em otimização combinatória.” 2009. Web. 25 Oct 2020.

Vancouver:

Pires MG. Abordagem neuro-genética para mapeamento de problemas de conexão em otimização combinatória. [Internet] [Doctoral dissertation]. University of São Paulo; 2009. [cited 2020 Oct 25]. Available from: http://www.teses.usp.br/teses/disponiveis/18/18153/tde-12062009-150911/ ;.

Council of Science Editors:

Pires MG. Abordagem neuro-genética para mapeamento de problemas de conexão em otimização combinatória. [Doctoral Dissertation]. University of São Paulo; 2009. Available from: http://www.teses.usp.br/teses/disponiveis/18/18153/tde-12062009-150911/ ;

12.
Tackx, Raphaël.
Analyse de la structure communautaire des réseaux bipartis : Analysis of the community structure in *bipartite* * networks*.

Degree: Docteur es, Informatique, 2018, Sorbonne université

URL: http://www.theses.fr/2018SORUS550

► Il existe dans le monde réel un nombre important de réseaux qui apparaissent naturellement, on les retrouve un peu partout, dans de nombreuses disciplines, par…
(more)

Subjects/Keywords: Théorie des graphes; Réseau réel; Détection de communautés; Graphe biparti; Réseau multi-couche; Réseaux sociaux du web; Graph theory; Real Network; Detection of communities; Bipartite graph; Multi-layer network; Social networks of the web; 006.754

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

APA (6^{th} Edition):

Tackx, R. (2018). Analyse de la structure communautaire des réseaux bipartis : Analysis of the community structure in bipartite networks. (Doctoral Dissertation). Sorbonne université. Retrieved from http://www.theses.fr/2018SORUS550

Chicago Manual of Style (16^{th} Edition):

Tackx, Raphaël. “Analyse de la structure communautaire des réseaux bipartis : Analysis of the community structure in bipartite networks.” 2018. Doctoral Dissertation, Sorbonne université. Accessed October 25, 2020. http://www.theses.fr/2018SORUS550.

MLA Handbook (7^{th} Edition):

Tackx, Raphaël. “Analyse de la structure communautaire des réseaux bipartis : Analysis of the community structure in bipartite networks.” 2018. Web. 25 Oct 2020.

Vancouver:

Tackx R. Analyse de la structure communautaire des réseaux bipartis : Analysis of the community structure in bipartite networks. [Internet] [Doctoral dissertation]. Sorbonne université; 2018. [cited 2020 Oct 25]. Available from: http://www.theses.fr/2018SORUS550.

Council of Science Editors:

Tackx R. Analyse de la structure communautaire des réseaux bipartis : Analysis of the community structure in bipartite networks. [Doctoral Dissertation]. Sorbonne université; 2018. Available from: http://www.theses.fr/2018SORUS550

13.
Yeung, Fiona.
Statistical Revealed Preference Models for *Bipartite* * Networks*.

Degree: Statistics, 2019, UCLA

URL: http://www.escholarship.org/uc/item/0fm6h8gm

► This dissertation focuses on investigating the driving factors behind the formation of connections in large two-mode *networks*. Assuming that network participants maximize their benefits, or…
(more)

Subjects/Keywords: Statistics; Economics; bipartite networks; computational statistics; discrete choice models; econometrics; game theory; matching theory

…of Simulated *Networks* . . . . . . . . . . . . . . . . . . .
70
4.3
Inclusive Values… …x29; and many-to-many relationships
with various τ values for both sides. All *networks* have… …*networks* with non-transferable utility (NTU) when the number of network
participants is… …alternative is observed, is not immediately applicable to two-mode *networks* where
the needed… …also
1
consider estimation under different types of matching in two-node *networks*: 1)…

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APA (6^{th} Edition):

Yeung, F. (2019). Statistical Revealed Preference Models for Bipartite Networks. (Thesis). UCLA. Retrieved from http://www.escholarship.org/uc/item/0fm6h8gm

Not specified: Masters Thesis or Doctoral Dissertation

Chicago Manual of Style (16^{th} Edition):

Yeung, Fiona. “Statistical Revealed Preference Models for Bipartite Networks.” 2019. Thesis, UCLA. Accessed October 25, 2020. http://www.escholarship.org/uc/item/0fm6h8gm.

Not specified: Masters Thesis or Doctoral Dissertation

MLA Handbook (7^{th} Edition):

Yeung, Fiona. “Statistical Revealed Preference Models for Bipartite Networks.” 2019. Web. 25 Oct 2020.

Vancouver:

Yeung F. Statistical Revealed Preference Models for Bipartite Networks. [Internet] [Thesis]. UCLA; 2019. [cited 2020 Oct 25]. Available from: http://www.escholarship.org/uc/item/0fm6h8gm.

Not specified: Masters Thesis or Doctoral Dissertation

Council of Science Editors:

Yeung F. Statistical Revealed Preference Models for Bipartite Networks. [Thesis]. UCLA; 2019. Available from: http://www.escholarship.org/uc/item/0fm6h8gm

Not specified: Masters Thesis or Doctoral Dissertation