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Heterogeneous network representation learning based on role feature extraction
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DOI：10.1016/j.patcog.2023.109870.png)
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摘要
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Since most of the real-world networks are heterogeneous, existing methods cannot characterize the roles of nodes in heterogeneous networks. The neighborhood structure of nodes in heterogeneous networks largely determines the node roles, and the basic statistical features of nodes describe the topology of nodes to some extent, so extracting structural features from the adjacency matrix of networks is crucial for role-oriented network representation learning(structural equivalence). Therefore, in this paper, we propose a heterogeneous network representation learning model based on role feature extraction, called HRFE(Heterogeneous Network Representation Learning for Role Feature Extraction). Firstly, we perform feature extraction for each node in the heterogeneous network to obtain a high-dimensional feature matrix, then perform role discovery using non-negative matrix decomposition techniques to obtain a role-based node representation, and finally verify the effectiveness of the model HRFE through experiments on a large number of real datasets.
Keyword:
Representation learning
Role discovery
Heterogeneous network
Matrix factorization
期刊
IF:
7.6
论文数:
1.3W
被引数:
4.5W
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