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Optimization Method for Robustness of Hypernetwork Communication with Integrated Structural Features.
Lei Chen1,2, Xiujuan Ma1,2, Fuxiang Ma1,2
1School of Computer Science, Qinghai Normal University, Xining 810008, China.
This study enhances hypernetwork communication robustness against attacks using four key indicators. Hyper-betweenness centrality proved most effective in improving network resilience.
Area of Science:
- Network Science
- Cybersecurity
- Complex Systems
Background:
- Hypernetworks are critical for telecommunication, public safety, and military applications, demanding high security and robustness.
- Ensuring communication robustness against external attacks and natural disasters is essential for these high-security networks.
Purpose of the Study:
- To integrate hypernetwork structural characteristics with an optimization method for enhancing communication robustness.
- To evaluate the effectiveness of four key indicators in improving hypernetwork resilience.
Main Methods:
- Combined four indicators: hyper-betweenness centrality, hyper-centrality of feature subgraph, hyper-centrality of Fiedler, and hyperdistance entropy.
- Conducted simulation experiments using Barabási-Albert (BA), Erdos-Renyi (ER), and Newman-Watts (NW) hypernetworks.
- Evaluated performance using best improvement performance (BIP_T) and derived hyperedge addition threshold θ.
Main Results:
- All four indicators improved hypernetwork communication robustness to varying degrees.
- Hyper-betweenness centrality showed the most significant optimization effect.
- Hyper-centrality of feature subgraph and hyper-centrality of Fiedler also demonstrated considerable optimization, while hyperdistance entropy had a weaker effect.
Conclusions:
- The proposed communication robustness optimization method, utilizing the four indicators, is effective and generalizable.
- The method was successfully applied to the WIKI-VOTE social hypernetwork, demonstrating broad applicability.

