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BGIC: A novel Bi-dimensional Gravitational Influence Centrality method for finding key nodes in signed hypergraphs

Author

Listed:
  • Tang, Zemiao
  • Liu, Guolin
  • Tian, Delong
  • Zhang, Yu
  • Qi, Xingqin

Abstract

Hypergraphs represent a mainstream method for data modeling, where the group interaction among multiple entities is modeled as a hyperedge. For example, customers rating the same restaurant can form a hyperedge. But whether the opinions are positive (recommend) or negative (criticize) has typically been overlooked, which may cause errors in the data mining process. To overcome this limitation, we adopt a new data structure, i.e., signed hypergraphs, for data modeling. Those recommended or critical opinions about a restaurant can be represented by positive or negative relationships between nodes and hyperedges, which significantly enriches the expressive power of hypergraph models.

Suggested Citation

  • Tang, Zemiao & Liu, Guolin & Tian, Delong & Zhang, Yu & Qi, Xingqin, 2026. "BGIC: A novel Bi-dimensional Gravitational Influence Centrality method for finding key nodes in signed hypergraphs," Chaos, Solitons & Fractals, Elsevier, vol. 202(P2).
    • Handle: RePEc:eee:chsofr:v:202:y:2026:i:p2:s0960077925016297
    DOI: 10.1016/j.chaos.2025.117616
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