Bicheng Yang, Donghang Tang, Qing Bao, Jian Xu, Heng Tian, Ming Jiang
2026.1.16INTELLIGENT DATA ANALYSIS
tlooto Summary
Bicliques serve to identify cohesive node groups in bipartite graphs, where each node is required to maintain at least <mml:mi>β</mml:mi>
Abstract
<jats:p> ( <jats:inline-formula> <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" display="inline" overflow="scroll"> <mml:mi>α</mml:mi> </mml:math> </jats:inline-formula> , <jats:inline-formula> <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" display="inline" overflow="scroll"> <mml:mi>β</mml:mi> </mml:math> </jats:inline-formula> )-bicliques serve to identify cohesive node groups in bipartite graphs, where each node is required to maintain at least <jats:inline-formula> <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" display="inline" overflow="scroll"> <mml:mi>α</mml:mi> </mml:math> </jats:inline-formula> or <jats:inline-formula> <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" display="inline" overflow="scroll"> <mml:mi>β</mml:mi> </mml:math> </jats:inline-formula> connections within the biclique. Although ( <jats:inline-formula> <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" display="inline" overflow="scroll"> <mml:mi>α</mml:mi> </mml:math> </jats:inline-formula> , <jats:inline-formula> <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" display="inline" overflow="scroll"> <mml:mi>β</mml:mi> </mml:math> </jats:inline-formula> )-biclique enumeration has been extensively studied, its temporal periodicity remains largely unexplored. To capture recurrent group behaviors in temporal bipartite graphs, we introduce the novel problem of periodic ( <jats:inline-formula> <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" display="inline" overflow="scroll"> <mml:mi>α</mml:mi> </mml:math> </jats:inline-formula> , <jats:inline-formula> <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" display="inline" overflow="scroll"> <mml:mi>β</mml:mi> </mml:math> </jats:inline-formula> )-biclique enumeration. Our approach presents three core innovations: First, a <jats:inline-formula> <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" display="inline" overflow="scroll"> <mml:mi>θ</mml:mi> </mml:math> </jats:inline-formula> -periodic biclique model, which enforces that all edges within a biclique exhibit <jats:inline-formula> <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" display="inline" overflow="scroll"> <mml:mi>θ</mml:mi> </mml:math> </jats:inline-formula> -periodic temporal support. Second, an edge-based reduction framework that avoids the combinatorial explosion typical in node-based enumeration. This is achieved by first constructing condensed subgraphs via <jats:inline-formula> <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" display="inline" overflow="scroll"> <mml:mi>θ</mml:mi> </mml:math> </jats:inline-formula> -temporal edge filtering, followed by biclique enumeration within the reduced space. Third, two optimized data structures: (i) a linked edge list (LE-list)–based periodic edge aggregator that enables constant-time subgraph access, and (ii) a Trie-based periodicity detector that accelerates the validation of temporal recurrence. Extensive experiments on six real-world temporal networks demonstrate up to a 10 <jats:inline-formula> <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" display="inline" overflow="scroll"> <mml:mo>×</mml:mo> </mml:math> </jats:inline-formula> speedup over baseline methods, highlighting substantial gains in efficiency, accuracy, and scalability. </jats:p>
Citation format
YANG, Bicheng, et al. Uncovering periodic group behaviors in temporal bipartite social graphs. INTELLIGENT DATA ANALYSIS, 2026.