CoreScope: Graph Mining Using k-Core Analysis--Patterns, Anomalies, and Algorithms

K. Shin, T. Eliassi-Rad, C. Faloutsos
Proceedings of the 16th IEEE ICDM'16
Barcelona, Spain, December 2016.
December 1, 2016


How do the  k-core structures of real-world graphs look like? What are the common  patterns and the anomalies? How can we use them for algorithm design and  applications? A k-core is the maximal subgraph where all vertices have degree  at least k. This concept has been applied to such diverse areas as  hierarchical structure analysis, graph visualization, and graph clustering.  Here, we explore pervasive patterns that are related to k-cores and emerging  in graphs from several diverse domains. Our discoveries are as follows: (1)  MIRROR PATTERN: coreness of vertices (i.e., maximum k such that each vertex  belongs to the k-core) is strongly correlated to their degree. (2)  CORETRIANGLE PATTERN: degeneracy of a graph (i.e., maximum k such that the  k-core exists in the graph) obeys a 3-to-1 power law with respect to the  count of triangles. (3) STRUCTURED CORE PATTERN: degeneracy-cores are not  cliques but have non-trivial structures such as core-periphery and  communities. Our algorithmic contributions show the usefulness of these  patterns. (1) CORE-A, which measures the deviation from MIRROR PATTERN,  successfully finds anomalies in real-world graphs complementing  densest-subgraph based anomaly detection methods. (2) CORE-D, a single-pass  streaming algorithm based on CORE-TRIANGLE PATTERN, accurately estimates the  degeneracy of billion-scale graphs up to 7× faster than a recent multipass  algorithm. (3) CORE-S, inspired by STRUCTURED CORE PATTERN, identifies  influential spreaders up to 17× faster than top competitors with comparable  accuracy.

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