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 33 articles )

Showing results 1 to 20 of 33.
Sorted by year (citations)

1 2 next

  1. 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
  2. Zhu, Yuanyuan; Hu, Bin; Chen, Lei; Dai, Qi: iMPTCE-Hnetwork: a multilabel classifier for identifying metabolic pathway types of chemicals and enzymes with a heterogeneous network (2021)
  3. Adriaens, Florian; De Bie, Tijl; Gionis, Aristides; Lijffijt, Jefrey; Matakos, Antonis; Rozenshtein, Polina: Relaxing the strong triadic closure problem for edge strength inference (2020)
  4. Aggarwal, Charu C.: Linear algebra and optimization for machine learning. A textbook (2020)
  5. Ananyeva, Marina; Makarov, Ilya; Pendiukhov, Mikhail: GSM: inductive learning on dynamic graph embeddings (2020)
  6. Benedek Rozemberczki, Oliver Kiss, Rik Sarkar: An API Oriented Open-source Python Framework for Unsupervised Learning on Graphs (2020) arXiv
  7. Han, Xiao; Zhang, Chunhong; Guo, Chenchen; Ji, Yang; Hu, Zheng: Distributed representation of knowledge graphs with subgraph-aware proximity (2020)
  8. Jisung Yoon, Kai-Cheng Yang, Woo-Sung Jung, Yong-Yeol Ahn: Persona2vec: A Flexible Multi-role Representations Learning Framework for Graphs (2020) arXiv
  9. Kazemi, Seyed Mehran; Goel, Rishab; Jain, Kshitij; Kobyzev, Ivan; Sethi, Akshay; Forsyth, Peter; Poupart, Pascal: Representation learning for dynamic graphs: 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. Lyu, Hanbaek; Needell, Deanna; Balzano, Laura: Online matrix factorization for Markovian data and applications to network dictionary learning (2020)
  13. Pio, Gianvito; Ceci, Michelangelo; Prisciandaro, Francesca; Malerba, Donato: Exploiting causality in gene network reconstruction based on graph embedding (2020)
  14. Škrlj, Blaž; Kralj, Jan; Lavrač, Nada: Embedding-based silhouette community detection (2020)
  15. van Engelen, Jesper E.; Hoos, Holger H.: A survey on semi-supervised learning (2020)
  16. Wang, Chenxu; Wang, Yang; Zhao, Zhiyuan; Qin, Dong; Luo, Xiapu; Qin, Tao: Credible seed identification for large-scale structural network alignment (2020)
  17. Zhou, Fan; Zhang, Kunpeng; Xie, Shuying; Luo, Xucheng: Learning to correlate accounts across online social networks: an embedding-based approach (2020)
  18. Do, Kien; Tran, Truyen; Nguyen, Thin; Venkatesh, Svetha: Attentional multilabel learning over graphs: a message passing approach (2019)
  19. Hui, Zhang; Yanchun, Liang; Cheng, Peng; Siyu, Han; Wei, Du; Ying, Li: Predicting lncRNA-disease associations using network topological similarity based on deep mining heterogeneous networks (2019)
  20. Kralj, Jan; Robnik-Sikonja, Marko; Lavrac, Nada: NetSDM: semantic data mining with network analysis (2019)

1 2 next