HSRF: Community Detection Based on Heterogeneous Attributes and Semi-Supervised Random Forest

Potential connections between complex networks need to be discovered by the network community detection. Current detection methods are commonly based on homogeneous information networks, which usually extract single information among the nodes of the complex network and will lead to incomplete information or information loss. To address these problems existing on community detection, we propose a novel method based on heterogeneous attributes and semi-supervised Random Forest (HSRF) inspired by heterogeneous information networks. We define heterogeneous attribute arrays of nodes, which reflect the structural relationships between nodes in complex networks. Semi-supervised learning based on Jaccard similarity coefficients is introduced to predict the noise points and solve the problem of anti-noise interference. Our experiments on real networks and synthetic standard networks show that HSRF improves the generalization of the undirected and directed network community detection. Moreover, our HSRF performs better in terms of robustness when the community boundary structure becomes more ambiguous and convenient for parallel processing.