Node2vec enhanced attributed graph matrix factorization algorithm

Zhiwen Zheng; Xiaoyun Chen

doi:10.1117/12.2684530

19 October 2023 Node2vec enhanced attributed graph matrix factorization algorithm

Zhiwen Zheng, Xiaoyun Chen

Proceedings Volume 12709, Fourth International Conference on Artificial Intelligence and Electromechanical Automation (AIEA 2023); 127093V (2023) https://doi.org/10.1117/12.2684530
Event: Fourth International Conference on Artificial Intelligence and Electromechanical Automation (AIEA 2023), 2023, Nanjing, China

Abstract

Community detection is an important tool to analyze and understand large-scale graph networks. Traditional non-negative matrix factorization method use the complete adjacency matrix, in which the redundant information may interfere with the learning of node features and bring high computational complexity. Thus, we propose the Node2vec enhanced attributed graph matrix factorization algorithm (N2V-AGMF) which combining the graph embedding and non-negative matrix factorization. The algorithm uses the Node2vec to extract node low-dimensional features based on topological information, then combines them with the attribute matrix for joint matrix factorization. The low dimensional embedding of the adjacency matrix enriches the representation of node features, and effectively reduces the high computational complexity caused by the factorization of the high dimensional matrix. Experiments were carried out on five real world datasets to verify the effectiveness of the algorithm.

Citation Download Citation

Zhiwen Zheng and Xiaoyun Chen "Node2vec enhanced attributed graph matrix factorization algorithm", Proc. SPIE 12709, Fourth International Conference on Artificial Intelligence and Electromechanical Automation (AIEA 2023), 127093V (19 October 2023); https://doi.org/10.1117/12.2684530

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