Optimization Method for Robustness of Hypernetwork Communication with Integrated Structural Features.

The ultimate objective of research on hypernetwork robustness is to enhance its capability to withstand external attacks and natural disasters. For hypernetworks such as telecommunication networks, public safety networks, and military networks-where security requirements are extremely high-achieving higher communication robustness is essential. This study integrates the structural characteristics of hypernetworks with an optimization method for communication robustness by combining four key indicators: hyper-betweenness centrality, hyper-centrality of feature subgraph, hyper-centrality of Fiedler, and hyperdistance entropy. Using the best improvement performance (BIP_T) as the evaluation metric, simulation experiments were conducted to comparatively analyze the effectiveness of these four indicators in enhancing the communication robustness of Barabási-Albert (BA), Erdos-Renyi (ER), and Newman-Watts (NW) hypernetworks, and theoretically derives the hyperedge addition threshold θ. The results show that all four indicators effectively improve the communication robustness of hypernetworks, although with varying degrees of optimization. Among them, hyper-betweenness centrality demonstrates the most significant optimization effect, followed by hyper-centrality of feature subgraph and hyper-centrality of Fiedler, while hyperdistance entropy exhibits a relatively weaker effect. Furthermore, these four indicators and the proposed communication robustness optimization method exhibit strong generalizability and have been effectively applied to the WIKI-VOTE social hypernetwork.