Community Search over Big Attributed Graphs (Xin Huang et al.)

Recently, community search over graphs has attracted significant attention and many algorithms have been developed for finding dense subgraphs from large graphs that contain given query nodes. In applications such as analysis of protein-protein interaction (PPI) networks, citation graphs, and collaboration networks, nodes tend to have attributes. Unfortunately, most previously developed community search algorithms ignore these attributes and result in communities with poor cohesion w.r.t. their node attributes.

In this project, we study the problem of attributed-driven community search on big attributed graph, that is, given an undirected graph G where nodes are associated with attributes, and an input query Q consisting of nodes Vq and attributes Wq, find the communities containing Vq, in which most community members are densely inter-connected and have similar attributes. In addition, we investigate the attributed-driven community search problem in a graph streaming setting with frequent insertions and deletions of graph vertices, edges, and also attributes. Finally, we will integrate all the above techniques, and we plan to propose an attributed-driven community query processing prototype system, which forms a foundation for attributed-driven community search analysis and processing.

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For further information on this research topic, please contact Dr. Xin Huang.