We are a research group within the Data Communication and Data Management Laboratory of the Department of Computer Science at the University of Texas at Dallas. Our Lab is headed by professor Ding-Zhu Du and Weili Wu. Our focus is the design and analysis of a variety of approaches for research problems generated in social networks. Currently, we are mainly working three topics on generated in real-world life scenarios. The first problem is about influence diffusion, in which influence represents news, ideas, information and so forth; the second one is about partitioning social networks into communities; and the third one is to predict the hidden or new created links between individuals in the future based on the existing or observed information.
Social Network Analysis
Social network analysis (SNA) is the methodical analysis of social networks. Social network analysis views social relationships in terms of network theory, consisting of nodes (representing individual actors within the network) and ties (which represent relationships between the individuals, such as friendship, kinship, organizational position, sexual relationships, etc.) These networks are often depicted in a social network diagram, where nodes are represented as points and ties are represented as lines. Recently SNA has received extensive attention among researchers across a wide range of disciplines such as computer science, physics and sociology, etc. For instance, it can be regarded as a platform for individuals communications; it can be used to investigate structure pattern of certain groups; it can be exploited to predict interactions between individuals that will appear in the future. Studies on these aspects lead promising steps for researchers across different fields.
We will work on tutorial on various techniques related to SNA. Several research directions are listed below:
1. Influence Diffusion Problem: We can compute the influence propagation probabilities by considering the the attributes of individuals, such as gender, age, interest and location and so on. We can design algorithms for the rumor blocking problem on models like SIS and SIR. We can integrate the time variable to the influence dissemination process.
2. Community Detection Problem: We can develop efficient algorithms to identify communities on different networks, such as facebook, mobile social network and weighted social networks, etc. We can design approaches by combining the edge prediction method, which is based on node similarity, with existing community detection algorithms. We can exploit algorithms to distinguish communities provided that the network information is incomplete.
3. Link Prediction Problem: We can predict links based on both local graph properties (path structure) and individuals features offered by large networks. We can modify the time-aware information to study the link prediction problem in other social networks. We can explore methods that may yield better performance and strengthen the existing connections between individuals via creating least number of new relations.
Data Communication and Data Management Laboratory
Department of Computer Science, University of Texas at Dallas
800 West Campbell Rd., Richardson, TX 75080, USA