Social Networks subgroup deals with problems concerning reallife networks. We develop scalable algorithms for networks that apply to applications ranging from viral marketing to product recommendations.
Area of Interest:
Social Network Analysis, Information Diffusion, Influence Maximization, Immunization, Recommendation Systems, Link Prediction, Graph Embedding
Information Diffusion
Influence Maximization with a Single Cascade
The study of information dissemination on a social network has gained significant importance with the rise of social media. Since the true dynamics are hidden, various diffusion models have been exposed to explain the cascading behavior. Such models require extensive simulation for estimating the dissemination over time. We have proposed a unified model which provides an approximate analytical solution to the problem of predicting probability of infection of every node in the network over time. Our model generalizes a large class of diffusion process. We demonstrate through extensive empirical evaluation that the error of approximation is small. We have built upon our unified model to develop an efficient method for influence maximization OSSUM.
Influence Maximization on Signed Networks
Often in marketing, political campaigns and social media, two competing products or opinions propagate over a social network. Studying social influence in such competing cascades scenarios enables building effective strategies for maximizing the propagation of one process by targeting the most “influential” nodes in the network. According to social theory, people tend to have similar opinions to their friends but opposite of their foes. Particularly, we study the progressive propagation of two competing cascades in a signed network (with friends and foe relationships), and provide an approximate analytical solution to compute the probability of infection of a node at any given time. We leverage our analytical solution to the problem of competing cascades in signed networks to develop a heuristic for the influence maximization problem OSSUM+.
Recent Publications

Ajitesh Srivastava, Charalampos Chelmis and Viktor K. Prasanna, Computing Competing Cascades on Signed Networks,
Social Network Analysis and Mining (SNAM '16) (To Appear).

Ajitesh Srivastava, Charalampos Chelmis and Viktor K. Prasanna, Heterogeneous Infection Rate SI model with Interregional Mobility,
NetSci X, 2016.

Ajitesh Srivastava, Charalampos Chelmis and Viktor K. Prasanna, Mining Large Dense Subgraphs,
Proceedings of the 25th International Conference Companion on World Wide Web (WWW '16), 2016.

Ajitesh Srivastava, Charalampos Chelmis and Viktor K. Prasanna, The Unified Model of Social Influence and its Application in Influence Maximization,
Social Network Analysis and Mining (SNAM '15), August 2015.

Ajitesh Srivastava, Charalampos Chelmis and Viktor K. Prasanna, Social Influence Computation and Maximization in Signed Networks with Competing Cascades,
Advances in Social Network Analysis and Mining (ASONAM '15), August 2015.

Charith Wickramaarachchi, Alok Kumbhare, Marc Frincu, Charalampos Chelmis and Viktor K. Prasanna,
Realtime Analytics for Fast Evolving Social Graphs,
IEEE/ACM International Symposium on Cluster, Cloud and Grid Computing (CCGrid), Scale challenge finalist, May 2015.

Charith Wickramaarachchi, Charalampos Chelmis and Viktor Prasanna,
Empowering Fast Incremental Computation over Large Scale Dynamic Graphs,
IEEE International Parallel & Distributed Processing Symposium (IPDPS) ParLearning 2015 Workshop, May 2015.

Charalampos Chelmis, Hao Wu, Vikram Sorathia, Viktor K. Prasanna,
Semantic Social Network Analysis for the Enterprise,
Journal of Computing and Informatics  Special Issue on Computational Intelligence for Business Collaboration, 2014.

C. Charalampos, A. Srivastava, and V. K. Prasanna,
Computational Models of Technology Adoption at the Workplace,
Social Network Analysis and Mining 4, no. 1 (2014): 118.

Alok Kumbhare, Marc Frincu, Cauligi Raghavendra, and Viktor Prasanna,
Efficient Extraction of High Centrality Vertices in Distributed Graphs,
IEEE 18th International Conference on High Performance Extreme Computing (HPEC), September 2014 (Best paper nomination).

Charith Wickramaarachchi, Marc Frincu, Patrick Small and Viktor Prasanna,
Fast Parallel Algorithm for Unfolding of Communities in Large Graphs,
IEEE 18th International Conference on High Performance Extreme Computing (HPEC), September 2014.

Yogesh Simmhan, Alok Kumbhare, Charith Wickramaarachchi, Soonil Nagarkar, Santosh Ravi, Cauligi Raghavendra and Viktor Prasanna,
GoFFish : A SubGraph Centric Framework for LargeScale Graph Analytics,
20th International EuroPar Conference (EuroPar), August 2014.

A. Srivastava, C. Chelmis, V. K. Prasanna,
Influence in Social Networks: A Unified Model?,
The IEEE/ACM International Conference on Social Networks Analysis and Mining (ASONAM), August 2014.
For the complete list of publications
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