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Modeling, Evaluation and Analysis of Dynamic Networks for Social Network Analysis

Junuthula, Ruthwik Reddy
http://orcid.org/0000-0003-1429-9501
http://rave.ohiolink.edu/etdc/view?acc_num=toledo1544819215833249

Abstract Details

2018, Doctor of Philosophy, University of Toledo, Engineering.
Many application settings involve the analysis of timestamped relations or events between a set of entities, e.g. messages between users of an on-line social network. Dynamic network models are typically used as analysis tools in these settings. They work by either aggregating events over time to form network snapshots, or model the network directly in continuous time. In dynamic network models the common problem researchers deal with is link prediction, which has been studied extensively in the literature, and many methods have been proposed. However On-line social networks (OSNs) often contain different types of relationships between users. When studying the structure of OSNs such as Facebook, two of the most commonly studied networks are friendship and interaction networks. The link prediction problem in friendship networks has been heavily researched. In Interaction networks where links are both added and removed over time, the link prediction or forecasting problem is more complex and involves predicting both newly added and newly removed links. This problem setting creates new challenges in the evaluation of dynamic link prediction methods. In this dissertation, I investigate several metrics currently used for evaluating the accuracy of dynamic link prediction methods and demonstrate why they are inappropriate and misleading in many cases. I provide several recommendations and propose a new metric to characterize link prediction accuracy fairly and effectively using a single number. The link prediction problem in interaction networks is still ongoing and in this Dissertation, I study the predictive power of combining friendship and interaction networks. By leveraging friendship networks, I show that I can improve the accuracy of link prediction in interaction networks. I observe that leveraging friendships improves the accuracy of predicting interactions between people that have never interacted before, but has little or no impact on interactions between people who have interacted before. Continuous time network analysis is a relatively new topic of research compared to discrete time analysis. In this dissertation, a block point process model (BPPM) for dynamic networks, which evolves in continuous time in the form of events at irregular time intervals is introduced. It is shown that networks generated by the BPPM follow a stochastic block model (SBM) in the limit of a growing number of nodes and this property is leveraged to develop an efficient inference procedure for the BPPM. The BPPM has been fitted to several real network data sets, using a local search and variational inference approaches, which enable our model to scale to networks several orders of magnitude compared to other existing point process network models.
Kevin Xu (Committee Chair)
Vijay Devabhaktuni (Committee Co-Chair)
Tian Chen (Committee Member)
Ahmad Javaid (Committee Member)
Scott Pappada (Committee Member)
87 p.
Computer Science; Mathematics; Sociology; Statistics
Variational Inference; Social Network Analysis; Continuous Time Network Models; Machine Learning; Stochastic Block Model; Local Search; Interaction Networks; Link Prediction

Recommended Citations

Citations

  • Junuthula, R. R. (2018). Modeling, Evaluation and Analysis of Dynamic Networks for Social Network Analysis [Doctoral dissertation, University of Toledo]. OhioLINK Electronic Theses and Dissertations Center. http://rave.ohiolink.edu/etdc/view?acc_num=toledo1544819215833249

    APA Style (7th edition)

  • Junuthula, Ruthwik Reddy. Modeling, Evaluation and Analysis of Dynamic Networks for Social Network Analysis. 2018. University of Toledo, Doctoral dissertation. OhioLINK Electronic Theses and Dissertations Center, http://rave.ohiolink.edu/etdc/view?acc_num=toledo1544819215833249.

    MLA Style (8th edition)

  • Junuthula, Ruthwik Reddy. "Modeling, Evaluation and Analysis of Dynamic Networks for Social Network Analysis." Doctoral dissertation, University of Toledo, 2018. http://rave.ohiolink.edu/etdc/view?acc_num=toledo1544819215833249

    Chicago Manual of Style (17th edition)

toledo1544819215833249
389
© 2018, some rights reserved.Creative Commons License Modeling, Evaluation and Analysis of Dynamic Networks for Social Network Analysis by Ruthwik Reddy Junuthula is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 3.0 Unported License. Based on a work at etd.ohiolink.edu.
This open access ETD is published by University of Toledo and OhioLINK.