DeepWalk

DeepWalk: Online Learning of Social Representations. We present DeepWalk, a novel approach for learning latent representations of vertices in a network. These latent representations encode social relations in a continuous vector space, which is easily exploited by statistical models. DeepWalk generalizes recent advancements in language modeling and unsupervised feature learning (or deep learning) from sequences of words to graphs. DeepWalk uses local information obtained from truncated random walks to learn latent representations by treating walks as the equivalent of sentences. We demonstrate DeepWalk’s latent representations on several multi-label network classification tasks for social networks such as BlogCatalog, Flickr, and YouTube. Our results show that DeepWalk outperforms challenging baselines which are allowed a global view of the network, especially in the presence of missing information. DeepWalk’s representations can provide F1 scores up to 10% higher than competing methods when labeled data is sparse. In some experiments, DeepWalk’s representations are able to outperform all baseline methods while using 60% less training data. DeepWalk is also scalable. It is an online learning algorithm which builds useful incremental results, and is trivially parallelizable. These qualities make it suitable for a broad class of real world applications such as network classification, and anomaly detection.


References in zbMATH (referenced in 63 articles )

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  1. Chen, Junyang; Gong, Zhiguo; Wang, Wei; Liu, Weiwen: HNS: hierarchical negative sampling for network representation learning (2021)
  2. Duan, Zhen; Sun, Xian; Zhao, Shu; Chen, Jie; Zhang, Yanping; Tang, Jie: Hierarchical community structure preserving approach for network embedding (2021)
  3. Guo, Xiaoyang; Srivastava, Anuj; Sarkar, Sudeep: A quotient space formulation for generative statistical analysis of graphical data (2021)
  4. Han, Xinyu; Zhao, Yi; Small, Michael: Revisiting the memory capacity in reservoir computing of directed acyclic network (2021)
  5. He, Jieyue; Wang, Jinmeng; Yu, Zhizhou: Attention based adversarially regularized learning for network embedding (2021)
  6. Kang, Bo; García García, Darío; Lijffijt, Jefrey; Santos-Rodríguez, Raúl; De Bie, Tijl: Conditional t-SNE: more informative t-SNE embeddings (2021)
  7. Liao, Zihan; Liang, Wenxin; Cui, Beilei; Liu, Xin: Structure-guided attributed network embedding with “centroid” enhancement (2021)
  8. 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)
  9. Ma, Guixiang; Ahmed, Nesreen K.; Willke, Theodore L.; Yu, Philip S.: Deep graph similarity learning: a survey (2021)
  10. Mercurio, Paula; Liu, Di: Identifying transition states of chemical kinetic systems using network embedding techniques (2021)
  11. Sanna Passino, Francesco; Bertiger, Anna S.; Neil, Joshua C.; Heard, Nicholas A.: Link prediction in dynamic networks using random dot product graphs (2021)
  12. Stankova, Marija; Praet, Stiene; Martens, David; Provost, Foster: Node classification over bipartite graphs through projection (2021)
  13. Wang, Lili; Huang, Chenghan; Ma, Weicheng; Liu, Ruibo; Vosoughi, Soroush: Hyperbolic node embedding for temporal networks (2021)
  14. Wang, Qingxiang; Kaliszyk, Cezary: JEFL: joint embedding of formal proof libraries (2021)
  15. Wang, Yuyao; Bu, Zhan; Yang, Huan; Li, Hui-Jia; Cao, Jie: An effective and scalable overlapping community detection approach: integrating social identity model and game theory (2021)
  16. Wei, Maosheng; Wu, Jun; Yang, Lina; Tang, Yuanyan: Matrix factorization with dual-network collaborative embedding for social recommendation (2021)
  17. Wei, Shaowei; Yu, Guoxian; Wang, Jun; Domeniconi, Carlotta; Zhang, Xiangliang: Multiple clusterings of heterogeneous information networks (2021)
  18. Xiao, Yunpeng; Li, Rui; Lu, Xingyu; Liu, Yanbing: Link prediction based on feature representation and fusion (2021)
  19. Xu, Mengjia: Understanding graph embedding methods and their applications (2021)
  20. Yang, Yiyang; Deng, Sucheng; Lu, Juan; Li, Yuhong; Gong, Zhiguo; U, Leong Hou; Hao, Zhifeng: GraphLSHC: towards large scale spectral hypergraph clustering (2021)

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