node2vec

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 83 articles )

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  1. Chen, Yiqi; Qian, Tieyun: Relation constrained attributed network embedding (2020)
  2. Chunaev, Petr: Community detection in node-attributed social networks: a survey (2020)
  3. Comin, Cesar H.; Peron, Thomas; Silva, Filipi N.; Amancio, Diego R.; Rodrigues, Francisco A.; Costa, Luciano da F.: Complex systems: features, similarity and connectivity (2020)
  4. Gao, Chongming; Zhang, Zhong; Huang, Chen; Yin, Hongzhi; Yang, Qinli; Shao, Junming: Semantic trajectory representation and retrieval via hierarchical embedding (2020)
  5. Han, Xiao; Zhang, Chunhong; Guo, Chenchen; Ji, Yang; Hu, Zheng: Distributed representation of knowledge graphs with subgraph-aware proximity (2020)
  6. Hou, Mingliang; Ren, Jing; Zhang, Da; Kong, Xiangjie; Zhang, Dongyu; Xia, Feng: Network embedding: taxonomies, frameworks and applications (2020)
  7. Jisung Yoon, Kai-Cheng Yang, Woo-Sung Jung, Yong-Yeol Ahn: Persona2vec: A Flexible Multi-role Representations Learning Framework for Graphs (2020) arXiv
  8. Kazemi, Seyed Mehran; Goel, Rishab; Jain, Kshitij; Kobyzev, Ivan; Sethi, Akshay; Forsyth, Peter; Poupart, Pascal: Representation learning for dynamic graphs: a survey (2020)
  9. Kumar, Ajay; Singh, Shashank Sheshar; Singh, Kuldeep; Biswas, Bhaskar: Link prediction techniques, applications, and performance: a survey (2020)
  10. Lavrač, Nada; Škrlj, Blaž; Robnik-Šikonja, Marko: Propositionalization and embeddings: two sides of the same coin (2020)
  11. Lee, O-Joun; Jung, Jason J.: Story embedding: learning distributed representations of stories based on character networks (2020)
  12. Li, Bentian; Pi, Dechang; Lin, Yunxia; Khan, Izhar Ahmed; Cui, Lin: Multi-source information fusion based heterogeneous network embedding (2020)
  13. Lv, Shaoqing; Xiang, Ju; Feng, Jingyu; Wang, Honggang; Lu, Guangyue; Li, Min: Community enhancement network embedding based on edge reweighting preprocessing (2020)
  14. Lyu, Hanbaek; Needell, Deanna; Balzano, Laura: Online matrix factorization for Markovian data and applications to network dictionary learning (2020)
  15. Meng, Lingqi; Masuda, Naoki: Analysis of node2vec random walks on networks (2020)
  16. Pio, Gianvito; Ceci, Michelangelo; Prisciandaro, Francesca; Malerba, Donato: Exploiting causality in gene network reconstruction based on graph embedding (2020)
  17. Shi, Min; Tang, Yufei; Zhu, Xingquan; Liu, Jianxun; He, Haibo: Topical network embedding (2020)
  18. Škrlj, Blaž; Kralj, Jan; Lavrač, Nada: Embedding-based silhouette community detection (2020)
  19. van Engelen, Jesper E.; Hoos, Holger H.: A survey on semi-supervised learning (2020)
  20. Vlassis, Nikolaos N.; Ma, Ran; Sun, WaiChing: Geometric deep learning for computational mechanics. I: Anisotropic hyperelasticity (2020)