# Social Networks

Social Networks subgroup deals with problems concerning real-life networks. We develop scalable algorithms for networks that apply to applications ranging from viral marketing to product recommendations.

Led by Dr. Viktor K. Prasanna

Social Networks subgroup deals with problems concerning real-life 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 the 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 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

Click here for the complete list of publications of all Labs under Prof. Viktor K. Prasanna.