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 79 articles )

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  1. Do, Kien; Tran, Truyen; Nguyen, Thin; Venkatesh, Svetha: Attentional multilabel learning over graphs: a message passing approach (2019)
  2. 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)
  3. Jaeger, Manfred; Lippi, Marco; Pellegrini, Giovanni; Passerini, Andrea: Counts-of-counts similarity for prediction and search in relational data (2019)
  4. Kawamoto, Tatsuro; Tsubaki, Masashi; Obuchi, Tomoyuki: Mean-field theory of graph neural networks in graph partitioning (2019)
  5. Kralj, Jan; Robnik-Sikonja, Marko; Lavrac, Nada: NetSDM: semantic data mining with network analysis (2019)
  6. Liao, Hao; Liu, Ming-Kai; Mariani, Manuel Sebastian; Zhou, Mingyang; Wu, Xingtong: Temporal similarity metrics for latent network reconstruction: the role of time-lag decay (2019)
  7. Nie, Binling; Sun, Shouqian: Context-dependent representation of knowledge graphs (2019)
  8. Sheikh, Nasrullah; Kefato, Zekarias; Montresor, Alberto: \textscgat2vec: representation learning for attributed graphs (2019)
  9. Tillquist, Richard C.; Lladser, Manuel E.: Low-dimensional representation of genomic sequences (2019)
  10. Xie, Yu; Gong, Maoguo; Qin, A. K.; Tang, Zedong; Fan, Xiaolong: TPNE: topology preserving network embedding (2019)
  11. Xie, Yu; Gong, Maoguo; Wang, Shanfeng; Liu, Wenfeng; Yu, Bin: Sim2vec: node similarity preserving network embedding (2019)
  12. Zhang, Daokun; Yin, Jie; Zhu, Xingquan; Zhang, Chengqi: Attributed network embedding via subspace discovery (2019)
  13. Aggarwal, Charu C.: Neural networks and deep learning. A textbook (2018)
  14. Ju, Cheng; Bibaut, Aurélien; van der Laan, Mark: The relative performance of ensemble methods with deep convolutional neural networks for image classification (2018)
  15. Leonardo F. R. Ribeiro, Pedro H. P. Savarese, Daniel R. Figueiredo: struc2vec: Learning Node Representations from Structural Identity (2017) arXiv
  16. Salehi Rizi, Fatemeh; Granitzer, Michael: Properties of vector embeddings in social networks (2017)
  17. William L. Hamilton, Rex Ying, Jure Leskovec: Inductive Representation Learning on Large Graphs (2017) arXiv
  18. Zeng, An; Shen, Zhesi; Zhou, Jianlin; Wu, Jinshan; Fan, Ying; Wang, Yougui; Stanley, H. Eugene: The science of science: from the perspective of complex systems (2017)
  19. Annamalai Narayanan, Mahinthan Chandramohan, Lihui Chen, Yang Liu, Santhoshkumar Saminathan: subgraph2vec: Learning Distributed Representations of Rooted Sub-graphs from Large Graphs (2016) arXiv