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Event detection in temporal social networks using a higher-order network model.

Xiang Li1, Xue Zhang1, Qizi Huangpeng1

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This study introduces novel higher-order network algorithms for improved event detection in temporal social networks. These methods enhance anomaly identification by modeling complex multivariate interactions more effectively than traditional approaches.

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Area of Science:

  • Complex network research
  • Temporal network analysis
  • Data mining and anomaly detection

Background:

  • Event detection in complex networks is crucial for identifying social events.
  • Traditional first-order network models struggle with multivariate sequential interactions and anomaly detection in temporal networks.

Purpose of the Study:

  • To propose novel algorithms for event detection in temporal social networks.
  • To address the limitations of traditional methods in capturing complex temporal dynamics.

Main Methods:

  • Developed two higher-order network algorithms: recovery higher-order network and innovation higher-order network.
  • Recovered multivariate sequential data from binary sequential data using chronological order.
  • Generated new multivariate sequential data using logical sequences.
  • Modeled multivariate sequential data using higher-order networks to identify interaction patterns.

Main Results:

  • The proposed higher-order network algorithms demonstrated significant performance improvements in event detection.
  • Effectively identified anomalies in temporal social networks by analyzing multivariate interaction patterns.
  • Outperformed traditional methods in accurately detecting social events.

Conclusions:

  • The developed higher-order network algorithms offer a more effective approach to event detection in temporal social networks.
  • These methods provide a new perspective on applying higher-order network models to temporal network analysis.
  • The findings highlight the potential of higher-order network models for understanding complex system dynamics and event detection.