网络与信息安全学报 (Jun 2024)
Hypernetwork link prediction method based on the SCL-CMM model
Abstract
An effective method for the internal interaction within modeling reality systems is provided by graphs; however, they have been unable to effectively display and capture the high-order heterogeneity that widely exists between multiple entities. Hypergraphs have been recognized for their ability to surpass the limitations imposed by low-order relationships. Hypernetwork link prediction, which involves predicting unknown hyperlinks based on the observed hypergraph structure, has increasingly become a hot topic in network science due to its capacity to fully describe the association patterns of complex systems. Existing methods typically design reasoning models for the entire topology, often overlooking the implicit aggregation characteristics within the network, which leads to an incomplete prediction of hyperlink categories. To address these issues, a coordination matrix minimization model based on hypergraph spectral clustering parser (SCL-CMM) was proposed. Initially, higher-order hypernetworks were mapped into heterogeneous hypergraphs with certain semantics. Subsequently, the spectral clustering parser was employed to extract the structural features of hyperlinks. The original hypergraph was reconstructed into multiple homoprotonic graphs, and the distribution of potential hyperlinks was inferred within the observation space of the subgraph, rather than the entire adjacency space, in order to restore the complete hypernetwork structure. This method federated learned the structural characteristics and aggregation attributes of hypernetworks to model the high-order nonlinear behavior of each subgraph, thereby solving the problems of single category and low precision in heterogeneous hypergraphs link prediction. Extensive comparative experiments were conducted on nine real datasets, demonstrating that this method significantly outperformed existing methods in terms of AUC score and recall rate.
