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Related Experiment Video

Updated: Aug 15, 2025

Author Spotlight: Advancing Large-Scale Neural Dynamics Through HD-MEA Technology
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I/O Efficient Early Bursting Cohesive Subgraph Discovery in Massive Temporal Networks.

Yuan Li1, Jie Dai1,2, Xiao-Lin Fan1

  • 1School of Information Science and Technology, North China University of Technology, Beijing, 100144 China.

Journal of Computer Science and Technology
|January 3, 2023
PubMed
Summary

This study introduces an early bursting cohesive subgraph (EBCS) model for temporal networks, enabling faster identification of critical events. The new model efficiently detects burstiness, outperforming existing methods in timeliness and resource usage.

Keywords:
I/O efficient algorithmearly bursting cohesive subgraph (EBCS)semi-external modeltemporal network

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Area of Science:

  • Graph theory and network analysis
  • Data mining and machine learning
  • Computational social science

Background:

  • Temporal networks capture dynamic relationships, crucial for understanding real-world phenomena.
  • Bursting cohesive subgraphs (BCS) identify rapidly intensifying events but existing methods lack timeliness.
  • Early detection of burstiness is vital for applications like emergency response and epidemic tracking.

Purpose of the Study:

  • To propose an early bursting cohesive subgraph (EBCS) model for timely identification of burstiness in temporal networks.
  • To develop efficient algorithms for finding EBCS, addressing limitations of existing BCS methods.
  • To enable analysis of massive temporal networks with limited memory resources.

Main Methods:

  • Constructing a time weight graph (TWG) integrating topological and temporal information.
  • Developing global search (GS-EBCS) and local search (LS-EBCS) algorithms for EBCS identification.
  • Designing I/O-efficient algorithms (I/O-GS, I/O-LS) for handling large-scale temporal networks under a semi-external model.

Main Results:

  • The proposed EBCS model effectively identifies bursty events earlier than traditional BCS methods.
  • GS-EBCS and LS-EBCS demonstrate efficiency in finding EBCS.
  • I/O-LS significantly reduces memory usage (e.g., 6.5 MB vs. 308.7 MB) for large datasets while maintaining comparable performance to LS-EBCS.

Conclusions:

  • The EBCS model provides a timely and effective approach to analyzing burstiness in temporal networks.
  • The developed algorithms, particularly I/O-LS, offer efficient solutions for analyzing massive temporal networks.
  • This research advances the understanding and detection of critical events in dynamic network data.