Heterogeneous Network Representation Learning Method Based on Meta-path

Yin, Ying; Ji, Lixin; Huang, Ruiyang; Cheng, Xiaoxing

doi:10.1109/icccbda.2019.8725667

Cited by 2 publications

(3 citation statements)

References 12 publications

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“…However, heterogeneous network embedding development momentum is rapid, and some research results have been achieved. The existing methods are mainly divided into three categories, methods based on decomposition [16][17][18][19][20], deep learning [21][22][23][24][25] and random walks [1,9,[13][14][15][26][27][28][29].…”

Section: Related Workmentioning

confidence: 99%

Heterogeneous Network Embedding Based on Random Walks of Type and Inner Constraint

Chen

Hao

et al. 2022

Mathematics

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In heterogeneous networks, random walks based on meta-paths require prior knowledge and lack flexibility. On the other hand, random walks based on non-meta-paths only consider the number of node types, but not the influence of schema and topology between node types in real networks. To solve these problems, this paper proposes a novel model HNE-RWTIC (Heterogeneous Network Embedding Based on Random Walks of Type and Inner Constraint). Firstly, to realize flexible walks, we design a Type strategy, which is a node type selection strategy based on the co-occurrence probability of node types. Secondly, to achieve the uniformity of node sampling, we design an Inner strategy, which is a node selection strategy based on the adjacency relationship between nodes. The Type and Inner strategy can realize the random walks based on meta-paths, the flexibility of the walks, and can sample the node types and nodes uniformly in proportion. Thirdly, based on the above strategy, a transition probability model is constructed; then, we obtain the nodes’ embedding based on the random walks and Skip-Gram. Finally, in the classification and clustering tasks, we conducted a thorough empirical evaluation of our method on three real heterogeneous networks. Experimental results show that HNE-RWTIC outperforms state-of-the-art approaches. In the classification task, in DBLP, AMiner-Top, and Yelp, the values of Micro-F1 and Macro-F1 of HNE-RWTIC are the highest: 2.25% and 2.43%, 0.85% and 0.99%, 3.77% and 5.02% higher than those of five other algorithms, respectively. In the clustering task, in DBLP, AMiner-Top, and Yelp networks, the NMI value is increased by 19.12%, 6.91%, and 0.04% at most, respectively.

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Section: Related Workmentioning

confidence: 99%

Heterogeneous Network Embedding Based on Random Walks of Type and Inner Constraint

Chen

Hao

et al. 2022

Mathematics

View full text Add to dashboard Cite

show abstract

“…At present, some methods for heterogeneous network embedding have been proposed, which are mainly divided into three categories, methods based on decomposition [11][12][13][14], deep learning [15][16][17][18][19][20] and random walks [21][22][23][24][25][26]. Among them, heterogeneous network embedding based on random walk is classical and widely used.…”

Section: Introductionmentioning

confidence: 99%

“…Among them, heterogeneous network embedding based on random walk is classical and widely used. It mostly relies on meta-paths guided random walks [22][23][24][25]. Meta-path is the embodiment of semantics in HINs.…”

Section: Introductionmentioning

confidence: 99%

Heterogeneous Network Embedding Based on Random Walks of Type & Inner Constraint

Chen¹,

Hao²,

Li³

et al. 2022

Preprint

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In heterogeneous networks, the random walks based on meta-path requires prior knowledge and lacks flexibility. And the random walks based on non-meta-path only considers the number of node types, but does not consider the influence of schema and topology between node types in real networks. To solve the above problems, this paper proposes a novel model HNE-RWTIC (Heterogeneous Network Embedding Based on Random Walks of Type & Inner Constraint). Firstly, to realize the flexible walks, we design a Type strategy, which is the node type selection strategy based on the co-occurrence probability of node types. Secondly, to achieve the uniformity of node sampling, we design an Inner strategy, which is the node selection strategy based on the adjacency relationship between nodes. The Type & Inner strategy can realize the random walks based on meta-path, the flexibility of the walks, and can sample the node types and nodes uniformly in proportion. Thirdly, based on the above strategy, a transition probability model is constructed; then, we obtain the nodes embedding based on the random walks and Skip-Gram. Finally, in classification and clustering tasks, we conducted a thorough empirical evaluation of our method on three real heterogeneous networks. Experimental results shown that F1-Score and NMI of HNE-RWTIC outperform state-of-the-art approaches.

show abstract

Heterogeneous Network Representation Learning Method Based on Meta-path

Cited by 2 publications

References 12 publications

Heterogeneous Network Embedding Based on Random Walks of Type and Inner Constraint

Heterogeneous Network Embedding Based on Random Walks of Type and Inner Constraint

Heterogeneous Network Embedding Based on Random Walks of Type & Inner Constraint

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