ECML PKDD '09 Proceedings of the European Conference on Machine Learning and Knowledge Discovery in Databases: Part I
Graph clustering based on structural/attribute similarities
Proceedings of the VLDB Endowment
Clustering Large Attributed Graphs: A Balance between Structural and Attribute Similarities
ACM Transactions on Knowledge Discovery from Data (TKDD)
Social Network Analysis and Mining for Business Applications
ACM Transactions on Intelligent Systems and Technology (TIST)
Discovering burst areas in fast evolving graphs
DASFAA'10 Proceedings of the 15th international conference on Database Systems for Advanced Applications - Volume Part I
Fires on the web: towards efficient exploring historical web graphs
DASFAA'10 Proceedings of the 15th international conference on Database Systems for Advanced Applications - Volume Part I
Mining most frequently changing component in evolving graphs
World Wide Web
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Graphs are popularly used to model structural relationships between objects. In many application domains such as social networks, sensor networks and telecommunication, graphs evolve over time. In this paper, we study a new problem of discovering the subgraphs that exhibit significant changes in evolving graphs. This problem is challenging since it is hard to define changing regions that are closely related to the actual changes (i.e., additions/deletions of edges/nodes) in graphs. We formalize the problem, and design an efficient algorithm that is able to identify the changing subgraphs incrementally. Our experimental results on real datasets show that our solution is very efficient and the resultant subgraphs are of high quality.