Abstract: Protein complexes are crucial for understanding cellular functions and identifying biological processes,playing critical roles in cell biology.The use of network clustering in PPI networks to identify protein complexes has become a hot research topic in data mining and bioinformatics.A variety of computational methods have emerged to identify protein complexes.However,most existing algorithms primarily use original network to detect dense subnetworks and fail to break through the limitations of traditional graph structures for multi-node interactions.Aiming at the issue of many-to-many complex interaction characteristics prevalent in biological networks,this paper proposes a novel protein complex identification method based on hypergraph network embedding(PCIHNE).Through the ability of hypergraph networks,it firstly converts the original PPI network into a hypergraph network.Then a hierarchical compression strategy recursively compresses the hypergraph into multiple smaller hypergraphs at different levels,thereby constructing a multi-scale analysis framework.Next,hypergraph convolution is performed on each levels to generate node representations at different granularities.These node representations are concatenated to obtain the complete node representation.Based on the representations obtained from hypergraph learning,a weighted PPI network is constructed by similarity on the original network.Finally,a core-attachment based strategy is used to obtain predicted protein complexes in the weighted PPI network.It evaluates the effectiveness of PCIHNE by comparing it with other protein complex algorithms on multiple yeast and human datasets.Experimental results demonstrate that PCIHNE is better than comparison protein complex identification methods regarding F-measure and Accuracy metrics.

Key words: Protein-protein interaction network, Protein complexes, Hypergraphs, Network embedding, Network clustering