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Graph pattern mining methods can extract informative and useful patterns fromlarge-scale graphs and capture underlying principles through the overwhelmedinformation. Contrast analysis serves as a keystone in various fields and hasdemonstrated its effectiveness in mining valuable information. However, it hasbeen long overlooked in graph pattern mining. Therefore, in this paper, weintroduce the concept of contrast subgraph, that is, a subset of nodes thathave significantly different edges or edge weights in two given graphs of thesame node set. The major challenge comes from the gap between the contrast andthe informativeness. Because of the widely existing noise edges in real-worldgraphs, the contrast may lead to subgraphs of pure noise. To avoid suchmeaningless subgraphs, we leverage the similarity as the cornerstone of thecontrast. Specifically, we first identify a coherent core, which is a smallsubset of nodes with similar edge structures in the two graphs, and then inducecontrast subgraphs from the coherent cores. Moreover, we design a generalfamily of coherence and contrast metrics and derive a polynomial-time algorithmto efficiently extract contrast subgraphs. Extensive experiments verify thenecessity of introducing coherent cores as well as the effectiveness andefficiency of our algorithm. Real-world applications demonstrate the tremendouspotentials of contrast subgraph mining.

Contrast Subgraph Mining from Coherent Cores