node2vec: Scalable Feature Learning for Networks. Prediction tasks over nodes and edges in networks require careful effort in engineering features used by learning algorithms. Recent research in the broader field of representation learning has led to significant progress in automating prediction by learning the features themselves. However, present feature learning approaches are not expressive enough to capture the diversity of connectivity patterns observed in networks. Here we propose node2vec, an algorithmic framework for learning continuous feature representations for nodes in networks. In node2vec, we learn a mapping of nodes to a low-dimensional space of features that maximizes the likelihood of preserving network neighborhoods of nodes. We define a flexible notion of a node’s network neighborhood and design a biased random walk procedure, which efficiently explores diverse neighborhoods. Our algorithm generalizes prior work which is based on rigid notions of network neighborhoods, and we argue that the added flexibility in exploring neighborhoods is the key to learning richer representations. We demonstrate the efficacy of node2vec over existing state-of-the-art techniques on multi-label classification and link prediction in several real-world networks from diverse domains. Taken together, our work represents a new way for efficiently learning state-of-the-art task-independent representations in complex networks.

References in zbMATH (referenced in 79 articles )

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  1. Chen, Jie; Saad, Yousef; Zhang, Zechen: Graph coarsening: from scientific computing to machine learning (2022)
  2. Liang, Bo; Wang, Lin; Wang, Xiaofan: OLMNE+FT: multiplex network embedding based on overlapping links (2022)
  3. Li, Xuan; Lu, Lin; Chen, Lei: Identification of protein functions in mouse with a label space partition method (2022)
  4. Meng, Lingqi; Masuda, Naoki: Epidemic dynamics on metapopulation networks with node2vec mobility (2022)
  5. Zhang, Yuan; Xia, Dong: Edgeworth expansions for network moments (2022)
  6. Chen, Jiaoyan; Hu, Pan; Jimenez-Ruiz, Ernesto; Holter, Ole Magnus; Antonyrajah, Denvar; Horrocks, Ian: \textttOWL2Vec*: embedding of OWL ontologies (2021)
  7. Chen, Junyang; Gong, Zhiguo; Wang, Wei; Liu, Weiwen: HNS: hierarchical negative sampling for network representation learning (2021)
  8. Duan, Zhen; Sun, Xian; Zhao, Shu; Chen, Jie; Zhang, Yanping; Tang, Jie: Hierarchical community structure preserving approach for network embedding (2021)
  9. Guo, Xiaoyang; Srivastava, Anuj; Sarkar, Sudeep: A quotient space formulation for generative statistical analysis of graphical data (2021)
  10. Haghir Chehreghani, Mostafa: Sublinear update time randomized algorithms for dynamic graph regression (2021)
  11. He, Jieyue; Wang, Jinmeng; Yu, Zhizhou: Attention based adversarially regularized learning for network embedding (2021)
  12. Kang, Bo; García García, Darío; Lijffijt, Jefrey; Santos-Rodríguez, Raúl; De Bie, Tijl: Conditional t-SNE: more informative t-SNE embeddings (2021)
  13. Lee, Yan-Li; Dong, Qiang; Zhou, Tao: Link prediction via controlling the leading eigenvector (2021)
  14. Liao, Zihan; Liang, Wenxin; Cui, Beilei; Liu, Xin: Structure-guided attributed network embedding with “centroid” enhancement (2021)
  15. Li, Jianxin; Ji, Cheng; Peng, Hao; He, Yu; Song, Yangqiu; Zhang, Xinmiao; Peng, Fanzhang: RWNE: a scalable random-walk based network embedding framework with personalized higher-order proximity preserved (2021)
  16. Ma, Guixiang; Ahmed, Nesreen K.; Willke, Theodore L.; Yu, Philip S.: Deep graph similarity learning: a survey (2021)
  17. Ma, Zheng; Xuan, Junyu; Wang, Yu Guang; Li, Ming; Liò, Pietro: Path integral based convolution and pooling for graph neural networks (2021)
  18. Mercurio, Paula; Liu, Di: Identifying transition states of chemical kinetic systems using network embedding techniques (2021)
  19. Sanna Passino, Francesco; Bertiger, Anna S.; Neil, Joshua C.; Heard, Nicholas A.: Link prediction in dynamic networks using random dot product graphs (2021)
  20. Stankova, Marija; Praet, Stiene; Martens, David; Provost, Foster: Node classification over bipartite graphs through projection (2021)

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