HIN2Vec: Explore Meta-paths in Heterogeneous Information Networks for Representation Learning
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Cites methods from "HIN2Vec: Explore Meta-paths in Hete..."
...[59] utilize a neural network model to capture rich relation semantics inHIN....
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Additional excerpts
...al [6] learn node embedding to capture rich relation semantics in HIN via neural network model....
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352 citations
Cites background or methods from "HIN2Vec: Explore Meta-paths in Hete..."
...fed to a skip-gram model [19] to generate node embeddings. Given user-defined metapaths, ESim [22] generates node embeddings by learning from sampled positive and negative metapath instances. HIN2vec [11] carries out multiple prediction training tasks to learn representations of nodes and metapaths of a heterogeneous graph. Given a metapath, HERec [23] converts a heterogeneousgraphintoahomogeneousgrap...
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...wing limitations. (1) The model does not leverage node content features, so it rarely performs well on heterogeneous graphs with rich node content features (e.g., metapath2vec [9], ESim [22], HIN2vec [11], and HERec [23]). (2) The model discards all intermediate nodes along the metapath by only considering two end nodes, which results in information loss (e.g., HERec [23] and HAN [31]). (3) The model ...
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References
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"HIN2Vec: Explore Meta-paths in Hete..." refers background in this paper
...Network data analysis and mining is an important research field because network data, capturing phenomena in various networks, such as social networks, paper citation networks, and World Wide Web, are ubiquitous in the real world [6, 15, 29]....
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33,771 citations
"HIN2Vec: Explore Meta-paths in Hete..." refers background in this paper
...Network data analysis and mining is an important research field because network data, capturing phenomena in various networks, such as social networks, paper citation networks, and World Wide Web, are ubiquitous in the real world [6, 15, 29]....
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33,301 citations
"HIN2Vec: Explore Meta-paths in Hete..." refers background in this paper
...Among the various approaches of representation learning, the neural network based learning models have received significant attention in recent years, and achieved successes in several empirical research studies of various domains, including speech recognition [12, 22], computer vision [9, 16], and natural language processing (NLP) [21]....
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24,012 citations
"HIN2Vec: Explore Meta-paths in Hete..." refers background in this paper
...Thus, while generating positive samples via random walks, we also generate negative data entries following the ideas of negative sampling in Word2Vec [21]....
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...Among the various approaches of representation learning, the neural network based learning models have received significant attention in recent years, and achieved successes in several empirical research studies of various domains, including speech recognition [12, 22], computer vision [9, 16], and natural language processing (NLP) [21]....
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