metapath2vec

metapath2vec: Scalable Representation Learning for Heterogeneous Networks. We study the problem of representation learning in heterogeneous networks. Its unique challenges come from the existence of multiple types of nodes and links, which limit the feasibility of the conventional network embedding techniques. We develop two scalable representation learning models, namely metapath2vec and metapath2vec++. The metapath2vec model formalizes meta-path-based random walks to construct the heterogeneous neighborhood of a node and then leverages a heterogeneous skip-gram model to perform node embeddings. The metapath2vec++ model further enables the simultaneous modeling of structural and semantic correlations in heterogeneous networks. Extensive experiments show that metapath2vec and metapath2vec++ are able to not only outperform state-of-the-art embedding models in various heterogeneous network mining tasks, such as node classification, clustering, and similarity search, but also discern the structural and semantic correlations between diverse network objects.


References in zbMATH (referenced in 11 articles )

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Sorted by year (citations)

  1. Šourek, Gustav; Železný, Filip; Kuželka, Ondřej: Beyond graph neural networks with lifted relational neural networks (2021)
  2. Stankova, Marija; Praet, Stiene; Martens, David; Provost, Foster: Node classification over bipartite graphs through projection (2021)
  3. Sun, Xin; Yu, Yongbo; Liang, Yao; Dong, Junyu; Plant, Claudia; Böhm, Christian: Fusing attributed and topological global-relations for network embedding (2021)
  4. Wei, Shaowei; Yu, Guoxian; Wang, Jun; Domeniconi, Carlotta; Zhang, Xiangliang: Multiple clusterings of heterogeneous information networks (2021)
  5. Yukuo Cen, Zhenyu Hou, Yan Wang, Qibin Chen, Yizhen Luo, Xingcheng Yao, Aohan Zeng, Shiguang Guo, Peng Zhang, Guohao Dai, Yu Wang, Chang Zhou, Hongxia Yang, Jie Tang: CogDL: An Extensive Toolkit for Deep Learning on Graphs (2021) arXiv
  6. Gao, Chongming; Zhang, Zhong; Huang, Chen; Yin, Hongzhi; Yang, Qinli; Shao, Junming: Semantic trajectory representation and retrieval via hierarchical embedding (2020)
  7. Kazemi, Seyed Mehran; Goel, Rishab; Jain, Kshitij; Kobyzev, Ivan; Sethi, Akshay; Forsyth, Peter; Poupart, Pascal: Representation learning for dynamic graphs: a survey (2020)
  8. Li, Bentian; Pi, Dechang; Lin, Yunxia; Khan, Izhar Ahmed; Cui, Lin: Multi-source information fusion based heterogeneous network embedding (2020)
  9. Lv, Shaoqing; Xiang, Ju; Feng, Jingyu; Wang, Honggang; Lu, Guangyue; Li, Min: Community enhancement network embedding based on edge reweighting preprocessing (2020)
  10. Sheikh, Nasrullah; Kefato, Zekarias; Montresor, Alberto: \textscgat2vec: representation learning for attributed graphs (2019)
  11. Wang, Chenguang; Song, Yangqiu; Li, Haoran; Zhang, Ming; Han, Jiawei: Unsupervised meta-path selection for text similarity measure based on heterogeneous information networks (2018)