swMATH ID: 37750
Software Authors: Tao-yang Fu, Wang-Chien Lee, Zhen Lei
Description: HIN2Vec: Explore Meta-paths in Heterogeneous Information Networks for Representation Learning. In this paper, we propose a novel representation learning framework, namely HIN2Vec, for heterogeneous information networks (HINs). The core of the proposed framework is a neural network model, also called HIN2Vec, designed to capture the rich semantics embedded in HINs by exploiting different types of relationships among nodes. Given a set of relationships specified in forms of meta-paths in an HIN, HIN2Vec carries out multiple prediction training tasks jointly based on a target set of relationships to learn latent vectors of nodes and meta-paths in the HIN. In addition to model design, several issues unique to HIN2Vec, including regularization of meta-path vectors, node type selection in negative sampling, and cycles in random walks, are examined. To validate our ideas, we learn latent vectors of nodes using four large-scale real HIN datasets, including Blogcatalog, Yelp, DBLP and U.S. Patents, and use them as features for multi-label node classification and link prediction applications on those networks. Empirical results show that HIN2Vec soundly outperforms the state-of-the-art representation learning models for network data, including DeepWalk, LINE, node2vec, PTE, HINE and ESim, by 6.6
Homepage: https://dl.acm.org/doi/abs/10.1145/3132847.3132953
Source Code:  https://github.com/csiesheep/hin2vec
Related Software: PTE; metapath2vec; node2vec; DGL; ArnetMiner; GraRep; PyTorch; DeepWalk; word2vec; Freebase; GraphSAINT; ProNE; RotatE; InfoGraph; DropEdge; NetSMF; GE-SpMM; BioGRID; Optuna; CogDL
Cited in: 5 Publications

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