Finding Patterns and Anomalies in Large Time-Evolving Graphs

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Social networks can be represented as time-evolving graphs where the nodes are the members/entities of the social network and the edges represent connections/relationships between the nodes. This project tries to answer questions such as: How does a "normal" social network look like? How will it evolve over time? How can we spot "abnormal" interactions (e.g., spam), in a time-evolving e-mail graph? The approach developed in this project is to look for time-evolution "laws", and to design fast, scalable data mining tools for real graphs with millions and billions of nodes. The approach consist of two efforts: (1) discovery of patterns that hold when graphs evolve over time and (2) tools to analyze, visualize and mine such graphs to discover anomalies. The resulting tools will have a broad applicability. They will be vital for mining and outlier detection in numerous settings, such as money-laundering rings, mis-configured routers on the Internet, suspicious user accesses to database records, surprising protein-protein interactions in a gene regulatory network, and many applications involving large-scale evolving social networks. The project Web site (http://www.cs.cmu.edu/~christos/PROJECTS/GRAPH-MINING/) provides additional information and will be used for results dissemination.