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• W2743013071 abstract "Nowadays, large-scale graph data is being generated in a variety of real-world applications, from social networks to co-authorship networks, from protein-protein interaction networks to road traffic networks. Many existing works on graph mining focus on the vertices and edges, with the first-order Markov chain as the underlying model. They fail to explore the high-order network structures, which are of key importance in many high impact domains. For example, in bank customer personally identifiable information (PII) networks, the star structures often correspond to a set of synthetic identities; in financial transaction networks, the loop structures may indicate the existence of money laundering. In this paper, we focus on mining user-specified high-order network structures and aim to find a structure-rich subgraph which does not break many such structures by separating the subgraph from the rest." @default.
• W2743013071 created "2017-08-17" @default.
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• W2743013071 date "2017-08-04" @default.
• W2743013071 modified "2023-10-16" @default.
• W2743013071 title "A Local Algorithm for Structure-Preserving Graph Cut" @default.
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• W2743013071 doi "https://doi.org/10.1145/3097983.3098015" @default.
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