| Summary: | Cohesive subgraph identification is a fundamental problem in bipartite graph analysis. In real applications, to better represent the co-relationship between entities, edges are usually associated with weights or frequencies, which are neglected by most existing research. To fill the gap, we propose a new cohesive subgraph model, <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><mo>(</mo><mi>k</mi><mo>,</mo><mi>ω</mi><mo>)</mo></mrow></semantics></math></inline-formula>-core, by considering both subgraph cohesiveness and frequency for weighted bipartite graphs. Specifically, <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><mo>(</mo><mi>k</mi><mo>,</mo><mi>ω</mi><mo>)</mo></mrow></semantics></math></inline-formula>-core requires each node on the left layer to have at least <i>k</i> neighbors (cohesiveness) and each node on the right layer to have a weight of at least <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mi>ω</mi></semantics></math></inline-formula> (frequency). In real scenarios, different users may have different parameter requirements. To handle massive graphs and queries, index-based strategies are developed. In addition, effective optimization techniques are proposed to improve the index construction phase. Compared with the baseline, extensive experiments on six datasets validate the superiority of our proposed methods.
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