Graph Embedding Based on Neighbor Similarity for Community Detection
Community detection is a crucial research topic in the realm of complex networks.Understanding and identifying the community structure of a network is essential for uncovering its behavior and function.In this paper,we propose a novel graph em-bedding method based on neighbor similarity for community detection.By utilizing the acceptance of nodes and aggregating attri-bute information expressions of neighbors,we obtain the vector representation of each node in the network.The final community de-tection results are then obtained by directly applying K-means clustering.Our experimental results demonstrate that our proposed al-gorithm outperforms other methods,showing significant improvements in both modularity and standard normalization metrics.
community detectionneighbor similaritygraph embeddingclustering
万方数据
维普
