subject:(submodular maximization)

Georgia Tech

1. Torrico Palacios, Alfredo Ignacio. Resource allocation and subset selection: new approaches at the interface between discrete and continuous optimization.

Degree: PhD, Industrial and Systems Engineering, 2019, Georgia Tech

URL: http://hdl.handle.net/1853/61720

► Resource allocation and subset selection are two relevant classes of problems in the core of combinatorial optimization. Over the past decade, there has been an… (more)

▼ Resource allocation and subset selection are two relevant classes of problems in the core of combinatorial optimization. Over the past decade, there has been an increased interest in these areas due to their significant impact in real-world applications. Online advertising, sharing-economy systems, and kidney exchange are a few examples of the applicability of resource allocation models. On the other hand, data summarization, influence modeling in social networks, and sensor location have extensively motivated the study of subset selection. In this thesis, we propose new insights to two classical problems of these areas: the online bipartite matching problem and the constrained submodular function maximization problem. In the first part of this thesis, we consider novel polyhedral approaches for the i.i.d. online variant of the classical bipartite matching problem defined as follows: one side of the bipartition is fixed and known in advance, while nodes from the other side appear one at a time as i.i.d. realizations of a uniform distribution and must immediately be matched or discarded. First, we obtain various static relaxations of the polyhedral set of achievable probabilities, introduce valid inequalities, and discuss when they are facet-defining. We also show how several of these relaxations correspond to ranking policies and their time-dependent generalizations. Later, we focus on dynamic polyhedral relaxations. We show how they theoretically dominate the static relaxations from the previous part and perform a polyhedral study to theoretically examine the strength of the new relaxations. We also discuss how to derive heuristic policies from the dual prices of the relaxations, in a similar fashion to dynamic resource prices used in network revenue management. In both approaches, static and dynamic, we demonstrate the empirical quality of these relaxations and policies via computational experiments. In the second part of this thesis, we focus on new approaches to the classical constrained submodular maximization problem. In the first section, we consider the robust variant of this problem, in which we optimize several submodular functions simultaneously subject to a wide class of combinatorial constraints. We consider both the offline setting where the data defining the problem is known in advance, as well as, the online setting where the input data is revealed over time. For both models we provide efficient bi-criteria algorithms with provable guarantees. In the second section, we explore the concept of sharpness in submodular function maximization. Empirical studies have shown that the performance of the greedy algorithm is substantially better in practice, even though its worst-case guarantee. Our goal is to define sharpness for submodular functions as a candidate explanation for this phenomenon. We show that the greedy algorithm provably performs better as the sharpness of the submodular function increases. Lastly, in both sections we present an exhaustive computational study to support our theoretical… Advisors/Committee Members: Singh, Mohit (advisor), Pokutta, Sebastian (advisor), Toriello, Alejandro (advisor), Ahmed, Shabbir (committee member), Vempala, Santosh (committee member).

Subjects/Keywords: Combinatorial optimization; Online optimization; Online bipartite matching; Constrained submodular maximization

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APA (6^th Edition):

Torrico Palacios, A. I. (2019). Resource allocation and subset selection: new approaches at the interface between discrete and continuous optimization. (Doctoral Dissertation). Georgia Tech. Retrieved from http://hdl.handle.net/1853/61720

Chicago Manual of Style (16^th Edition):

Torrico Palacios, Alfredo Ignacio. “Resource allocation and subset selection: new approaches at the interface between discrete and continuous optimization.” 2019. Doctoral Dissertation, Georgia Tech. Accessed April 17, 2021. http://hdl.handle.net/1853/61720.

MLA Handbook (7^th Edition):

Torrico Palacios, Alfredo Ignacio. “Resource allocation and subset selection: new approaches at the interface between discrete and continuous optimization.” 2019. Web. 17 Apr 2021.

Vancouver:

Torrico Palacios AI. Resource allocation and subset selection: new approaches at the interface between discrete and continuous optimization. [Internet] [Doctoral dissertation]. Georgia Tech; 2019. [cited 2021 Apr 17]. Available from: http://hdl.handle.net/1853/61720.

Council of Science Editors:

Torrico Palacios AI. Resource allocation and subset selection: new approaches at the interface between discrete and continuous optimization. [Doctoral Dissertation]. Georgia Tech; 2019. Available from: http://hdl.handle.net/1853/61720

Iowa State University

2. Rajagopal Padmanabhan, Madhavan. Spreading information in social networks containing adversarial users.

Degree: 2020, Iowa State University

URL: https://lib.dr.iastate.edu/etd/18384

► In the modern day, social networks have become an integral part of how people communicate information and ideas. Consequently, leveraging the network to maximize information… (more)

Subjects/Keywords: Influence Maximization; Information Diffusion; Social Network Analysis; Submodular Optimization

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APA (6^th Edition):

Rajagopal Padmanabhan, M. (2020). Spreading information in social networks containing adversarial users. (Thesis). Iowa State University. Retrieved from https://lib.dr.iastate.edu/etd/18384

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):

Rajagopal Padmanabhan, Madhavan. “Spreading information in social networks containing adversarial users.” 2020. Thesis, Iowa State University. Accessed April 17, 2021. https://lib.dr.iastate.edu/etd/18384.

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

MLA Handbook (7^th Edition):

Rajagopal Padmanabhan, Madhavan. “Spreading information in social networks containing adversarial users.” 2020. Web. 17 Apr 2021.

Vancouver:

Rajagopal Padmanabhan M. Spreading information in social networks containing adversarial users. [Internet] [Thesis]. Iowa State University; 2020. [cited 2021 Apr 17]. Available from: https://lib.dr.iastate.edu/etd/18384.

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

Council of Science Editors:

Rajagopal Padmanabhan M. Spreading information in social networks containing adversarial users. [Thesis]. Iowa State University; 2020. Available from: https://lib.dr.iastate.edu/etd/18384

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

University of Pennsylvania

3. Gillenwater, Jennifer Ann. Approximate Inference for Determinantal Point Processes.

Degree: 2014, University of Pennsylvania

URL: https://repository.upenn.edu/edissertations/1285

► In this thesis we explore a probabilistic model that is well-suited to a variety of subset selection tasks: the determinantal point process (DPP). DPPs were… (more)

▼ In this thesis we explore a probabilistic model that is well-suited to a variety of subset selection tasks: the determinantal point process (DPP). DPPs were originally developed in the physics community to describe the repulsive interactions of fermions. More recently, they have been applied to machine learning problems such as search diversification and document summarization, which can be cast as subset selection tasks. A challenge, however, is scaling such DPP-based methods to the size of the datasets of interest to this community, and developing approximations for DPP inference tasks whose exact computation is prohibitively expensive. A DPP defines a probability distribution over all subsets of a ground set of items. Consider the inference tasks common to probabilistic models, which include normalizing, marginalizing, conditioning, sampling, estimating the mode, and maximizing likelihood. For DPPs, exactly computing the quantities necessary for the first four of these tasks requires time cubic in the number of items or features of the items. In this thesis, we propose a means of making these four tasks tractable even in the realm where the number of items and the number of features is large. Specifically, we analyze the impact of randomly projecting the features down to a lower-dimensional space and show that the variational distance between the resulting DPP and the original is bounded. In addition to expanding the circumstances in which these first four tasks are tractable, we also tackle the other two tasks, the first of which is known to be NP-hard (with no PTAS) and the second of which is conjectured to be NP-hard. For mode estimation, we build on submodular maximization techniques to develop an algorithm with a multiplicative approximation guarantee. For likelihood maximization, we exploit the generative process associated with DPP sampling to derive an expectation-maximization (EM) algorithm. We experimentally verify the practicality of all the techniques that we develop, testing them on applications such as news and research summarization, political candidate comparison, and product recommendation.

Subjects/Keywords: determinantal point processes; expectation-maximization; learning; MAP inference; random projections; submodular maximization; Computer Sciences; Engineering; Statistics and Probability

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APA (6^th Edition):

Gillenwater, J. A. (2014). Approximate Inference for Determinantal Point Processes. (Thesis). University of Pennsylvania. Retrieved from https://repository.upenn.edu/edissertations/1285

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):

Gillenwater, Jennifer Ann. “Approximate Inference for Determinantal Point Processes.” 2014. Thesis, University of Pennsylvania. Accessed April 17, 2021. https://repository.upenn.edu/edissertations/1285.

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

MLA Handbook (7^th Edition):

Gillenwater, Jennifer Ann. “Approximate Inference for Determinantal Point Processes.” 2014. Web. 17 Apr 2021.

Vancouver:

Gillenwater JA. Approximate Inference for Determinantal Point Processes. [Internet] [Thesis]. University of Pennsylvania; 2014. [cited 2021 Apr 17]. Available from: https://repository.upenn.edu/edissertations/1285.

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

Council of Science Editors:

Gillenwater JA. Approximate Inference for Determinantal Point Processes. [Thesis]. University of Pennsylvania; 2014. Available from: https://repository.upenn.edu/edissertations/1285

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

Virginia Tech

4. Zhou, Lifeng. Parsimonious, Risk-Aware, and Resilient Multi-Robot Coordination.

Degree: PhD, Electrical Engineering, 2020, Virginia Tech

URL: http://hdl.handle.net/10919/98596

► Today, robotics and autonomous systems have been increasingly used in various areas such as manufacturing, military, agriculture, medical sciences, and environmental monitoring. However, most of… (more)

Subjects/Keywords: Multi-Robot Systems; Resilient and Risk-aware Submodular Maximization; Planning and Assignment Algorithms; Event-Triggered Control

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

APA (6^th Edition):

Zhou, L. (2020). Parsimonious, Risk-Aware, and Resilient Multi-Robot Coordination. (Doctoral Dissertation). Virginia Tech. Retrieved from http://hdl.handle.net/10919/98596

Chicago Manual of Style (16^th Edition):

Zhou, Lifeng. “Parsimonious, Risk-Aware, and Resilient Multi-Robot Coordination.” 2020. Doctoral Dissertation, Virginia Tech. Accessed April 17, 2021. http://hdl.handle.net/10919/98596.

MLA Handbook (7^th Edition):

Zhou, Lifeng. “Parsimonious, Risk-Aware, and Resilient Multi-Robot Coordination.” 2020. Web. 17 Apr 2021.

Vancouver:

Zhou L. Parsimonious, Risk-Aware, and Resilient Multi-Robot Coordination. [Internet] [Doctoral dissertation]. Virginia Tech; 2020. [cited 2021 Apr 17]. Available from: http://hdl.handle.net/10919/98596.

Council of Science Editors:

Zhou L. Parsimonious, Risk-Aware, and Resilient Multi-Robot Coordination. [Doctoral Dissertation]. Virginia Tech; 2020. Available from: http://hdl.handle.net/10919/98596

University of Washington

5. Raut, Prasanna Sanjay. Online Decision Making: DR-Submodular Objectives and Stochastic Linear Constraints.

Degree: 2021, University of Washington

URL: http://hdl.handle.net/1773/46846

► In this thesis, we consider online continuous DR-submodular maximization with linear stochastic long-term constraints. Compared to the prior work on online submodular maximization , our… (more)

Subjects/Keywords: regret analysis; non-convex optimization; online optimization; submodular maximization; Applied mathematics; Computer science; Operations research; Mechanical engineering

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APA (6^th Edition):

Raut, P. S. (2021). Online Decision Making: DR-Submodular Objectives and Stochastic Linear Constraints. (Thesis). University of Washington. Retrieved from http://hdl.handle.net/1773/46846

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):

Raut, Prasanna Sanjay. “Online Decision Making: DR-Submodular Objectives and Stochastic Linear Constraints.” 2021. Thesis, University of Washington. Accessed April 17, 2021. http://hdl.handle.net/1773/46846.

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

MLA Handbook (7^th Edition):

Raut, Prasanna Sanjay. “Online Decision Making: DR-Submodular Objectives and Stochastic Linear Constraints.” 2021. Web. 17 Apr 2021.

Vancouver:

Raut PS. Online Decision Making: DR-Submodular Objectives and Stochastic Linear Constraints. [Internet] [Thesis]. University of Washington; 2021. [cited 2021 Apr 17]. Available from: http://hdl.handle.net/1773/46846.

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

Council of Science Editors:

Raut PS. Online Decision Making: DR-Submodular Objectives and Stochastic Linear Constraints. [Thesis]. University of Washington; 2021. Available from: http://hdl.handle.net/1773/46846

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

6. Ward, Justin. Oblivious and Non-oblivious Local Search for Combinatorial Optimization.

Degree: 2012, University of Toronto

URL: http://hdl.handle.net/1807/34957

►

Standard local search algorithms for combinatorial optimization problems repeatedly apply small changes to a current solution to improve the problem's given objective function. In contrast,… (more)

▼

Standard local search algorithms for combinatorial optimization problems repeatedly apply small changes to a current solution to improve the problem's given objective function. In contrast, non-oblivious local search algorithms are guided by an auxiliary potential function, which is distinct from the problem's objective. In this thesis, we compare the standard and non-oblivious approaches for a variety of problems, and derive new, improved non-oblivious local search algorithms for several problems in the area of constrained linear and monotone submodular maximization. First, we give a new, randomized approximation algorithm for maximizing a monotone submodular function subject to a matroid constraint. Our algorithm's approximation ratio matches both the known hardness of approximation bounds for the problem and the performance of the recent ``continuous greedy'' algorithm. Unlike the continuous greedy algorithm, our algorithm is straightforward and combinatorial. In the case that the monotone submodular function is a coverage function, we can obtain a further simplified, deterministic algorithm with improved running time. Moving beyond the case of single matroid constraints, we then consider general classes of set systems that capture problems that can be approximated well. While previous such classes have focused primarily on greedy algorithms, we give a new class that captures problems amenable to optimization by local search algorithms. We show that several combinatorial optimization problems can be placed in this class, and give a non-oblivious local search algorithm that delivers improved approximations for a variety of specific problems. In contrast, we show that standard local search algorithms give no improvement over known approximation results for these problems, even when allowed to search larger neighborhoods than their non-oblivious counterparts. Finally, we expand on these results by considering standard local search algorithms for constraint satisfaction problems. We develop conditions under which the approximation ratio of standard local search remains limited even for super-polynomial or exponential local neighborhoods. In the special case of MaxCut, we further show that a variety of techniques including random or greedy initialization, large neighborhoods, and best-improvement pivot rules cannot improve the approximation performance of standard local search.

PhD

Advisors/Committee Members: Borodin, Allan, Computer Science.

Subjects/Keywords: Approximation Algorithms; Local Search; Submodular Maximization; Independence Systems; 0984

…new areas, including submodular maximization, and obtain improved approximation for a… …for monotone submodular maximization subject to a matroid constraint. Moreover, if the total… …problem of linear and monotone submodular maximization in larger classes of set systems. There… …submodular maximization subject to a uniform matroid constraint. They show that a greedy algorithm… …Preliminaries for submodular maximization are summarized in a survey of Goundan and Schulz [46…

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

APA (6^th Edition):

Ward, J. (2012). Oblivious and Non-oblivious Local Search for Combinatorial Optimization. (Doctoral Dissertation). University of Toronto. Retrieved from http://hdl.handle.net/1807/34957

Chicago Manual of Style (16^th Edition):

Ward, Justin. “Oblivious and Non-oblivious Local Search for Combinatorial Optimization.” 2012. Doctoral Dissertation, University of Toronto. Accessed April 17, 2021. http://hdl.handle.net/1807/34957.

MLA Handbook (7^th Edition):

Ward, Justin. “Oblivious and Non-oblivious Local Search for Combinatorial Optimization.” 2012. Web. 17 Apr 2021.

Vancouver:

Ward J. Oblivious and Non-oblivious Local Search for Combinatorial Optimization. [Internet] [Doctoral dissertation]. University of Toronto; 2012. [cited 2021 Apr 17]. Available from: http://hdl.handle.net/1807/34957.

Council of Science Editors:

Ward J. Oblivious and Non-oblivious Local Search for Combinatorial Optimization. [Doctoral Dissertation]. University of Toronto; 2012. Available from: http://hdl.handle.net/1807/34957

Georgia Tech

7. Du, Nan. Modeling, learning, and inference of high-dimensional asynchronous event data.

Degree: PhD, Computational Science and Engineering, 2016, Georgia Tech

URL: http://hdl.handle.net/1853/55568

► The increasing availability of temporal-spatial events produced from natural and social systems provides new opportunities and challenges for effective modeling the latent dynamics which inherently… (more)

▼ The increasing availability of temporal-spatial events produced from natural and social systems provides new opportunities and challenges for effective modeling the latent dynamics which inherently govern these seemly ``random'' data. In our work, we propose a unified probabilistic framework based on multivariate point processes to better predict `who will do what by when and where?' in the future. This framework comprises a systematic paradigm for modeling, learning, and making inference of large-scale asynchronous high-dimensional event data. With this common framework, we contribute in the following three aspects. Accurate Modeling: we first propose non-parametric and topic-modulated multivariate terminating point processes to capture continuous-time heterogeneous information diffusions. We then develop the low-rank Hawkes process to describe the recurrent temporal interactions among different types of entities. We also build a link between the recurrent neural network and the temporal point process to learn a general representation of the influence from the past event history. Finally, we establish a previously unexplored connection between Bayesian Nonparametrics and temporal point processes to jointly model the temporal data and other type of additional information. Efficient Learning: we develop a robust structure learning algorithm via group lasso, which is able to efficiently uncover sparse heterogeneous interdependent relations specified via vectorized parameters among the dimensions. We also propose an efficient nonnegative matrix rank minimization algorithm, which elegantly inherits the advantages from both the proximal methods and the conditional gradient methods to solve the matrix rank minimization problem under different constraints. Finally, in the data streaming setting, we develop a Bayesian inference algorithm for inferring latent variables and updating the respective model parameters based on both temporal and textual information, which achieves almost constant processing time per data sample. Scalable Inference: another important aspect of our research is to make future predictions by exploiting the learned models. Specifically, based on the terminating processes, we develop the first scalable influence estimation algorithm in continuous-time diffusion networks with provable performance guarantees. Based on the low-rank Hawkes processes, we develop the first time-sensitive recommendation algorithm, which not only can recommend the most relevant item specific to a given moment, but also can predict the next returning time for a user to a designated service. Finally, based on the recurrent point processes, we have derived an analytic solution to shape the overall network activities of users. We show that our method can provide fine-grained control over user activities in a time-sensitive fashion. Advisors/Committee Members: Zha, Hongyuan (advisor), Song, Le (committee member), Hu, Yu (committee member), Faloutsos, Christos (committee member), Gabrilovich, Evgeniy (committee member).

Subjects/Keywords: Multivariate point process; Hawkes process; Survival analysis; Poisson process; Dirichlet process; Low-rank models; Social network analysis; Network structure inference; Information diffusion; Influence estimation; Influence maximization; Submodular maximization; Document clustering; Recommender systems

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APA (6^th Edition):

Du, N. (2016). Modeling, learning, and inference of high-dimensional asynchronous event data. (Doctoral Dissertation). Georgia Tech. Retrieved from http://hdl.handle.net/1853/55568

Chicago Manual of Style (16^th Edition):

Du, Nan. “Modeling, learning, and inference of high-dimensional asynchronous event data.” 2016. Doctoral Dissertation, Georgia Tech. Accessed April 17, 2021. http://hdl.handle.net/1853/55568.

MLA Handbook (7^th Edition):

Du, Nan. “Modeling, learning, and inference of high-dimensional asynchronous event data.” 2016. Web. 17 Apr 2021.

Vancouver:

Du N. Modeling, learning, and inference of high-dimensional asynchronous event data. [Internet] [Doctoral dissertation]. Georgia Tech; 2016. [cited 2021 Apr 17]. Available from: http://hdl.handle.net/1853/55568.

Council of Science Editors:

Du N. Modeling, learning, and inference of high-dimensional asynchronous event data. [Doctoral Dissertation]. Georgia Tech; 2016. Available from: http://hdl.handle.net/1853/55568

Kyoto University

8. Sakaue, Shinsaku. Online, Submodular, and Polynomial Optimization with Discrete Structures .

Degree: 2020, Kyoto University

URL: http://hdl.handle.net/2433/253424

Subjects/Keywords: Discrete structures; Zero-suppressed binary decision diagrams; Online combinatorial optimization; Submodular function maximization; Binary polynomial optimization

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

APA (6^th Edition):

Sakaue, S. (2020). Online, Submodular, and Polynomial Optimization with Discrete Structures . (Thesis). Kyoto University. Retrieved from http://hdl.handle.net/2433/253424

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):

Sakaue, Shinsaku. “Online, Submodular, and Polynomial Optimization with Discrete Structures .” 2020. Thesis, Kyoto University. Accessed April 17, 2021. http://hdl.handle.net/2433/253424.

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

MLA Handbook (7^th Edition):

Sakaue, Shinsaku. “Online, Submodular, and Polynomial Optimization with Discrete Structures .” 2020. Web. 17 Apr 2021.

Vancouver:

Sakaue S. Online, Submodular, and Polynomial Optimization with Discrete Structures . [Internet] [Thesis]. Kyoto University; 2020. [cited 2021 Apr 17]. Available from: http://hdl.handle.net/2433/253424.

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

Council of Science Editors:

Sakaue S. Online, Submodular, and Polynomial Optimization with Discrete Structures . [Thesis]. Kyoto University; 2020. Available from: http://hdl.handle.net/2433/253424

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

9. Aslay, Çiğdem. From viral marketing to social advertising: ad allocation under social influence.

Degree: Departament de Tecnologies de la Informació i les Comunicacions, 2016, Universitat Pompeu Fabra

URL: http://hdl.handle.net/10803/401679

► Esta tesis constituye una de las primeras investigaciones en la intersección entre propagación de influencia social, marketing viral y publicidad social. El objetivo es sacar… (more)

Subjects/Keywords: Influence maximization; Viral marketing; Social advertising; Submodular functions; Social influence; Similarity search; Reverse reachability sampling; Approximation algorithms; Social networks; Influència de maximització; Màrqueting viral; Publicitat social; Funcions submodulars; Influència social; Recerca de similitud; Presa de mostres d'accessibilitat inversa; Algoritmes d'aproximació; Xarxes socials; 62

…is NP-hard and it corresponds to the problem of monotone submodular function maximization… …109] for the maximization of a monotone submodular subject to a cardinality constraint… …Influence Maximization in Social Networks . 2.2 Modeling and Learning Influence Propagation 2.3… …7 7 9 11 12 Online Topic-aware Influence Maximization Queries 3.1 Introduction… …3.2 Related Work . . . . . . . . . . . . . . . . . . . 3.2.1 Influence Maximization…

12 more images…

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

APA (6^th Edition):

Aslay, �. (2016). From viral marketing to social advertising: ad allocation under social influence. (Thesis). Universitat Pompeu Fabra. Retrieved from http://hdl.handle.net/10803/401679

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):

Aslay, Çiğdem. “From viral marketing to social advertising: ad allocation under social influence.” 2016. Thesis, Universitat Pompeu Fabra. Accessed April 17, 2021. http://hdl.handle.net/10803/401679.

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

MLA Handbook (7^th Edition):

Aslay, Çiğdem. “From viral marketing to social advertising: ad allocation under social influence.” 2016. Web. 17 Apr 2021.

Vancouver:

Aslay �. From viral marketing to social advertising: ad allocation under social influence. [Internet] [Thesis]. Universitat Pompeu Fabra; 2016. [cited 2021 Apr 17]. Available from: http://hdl.handle.net/10803/401679.

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

Council of Science Editors:

Aslay �. From viral marketing to social advertising: ad allocation under social influence. [Thesis]. Universitat Pompeu Fabra; 2016. Available from: http://hdl.handle.net/10803/401679

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

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