GraRep: Learning Graph Representations with Global Structural Information. In this paper, we present GraRep, a novel model for learning vertex representations of weighted graphs. This model learns low dimensional vectors to represent vertices appearing in a graph and, unlike existing work, integrates global structural information of the graph into the learning process. We also formally analyze the connections between our work and several previous research efforts, including the DeepWalk model of Perozzi et al. as well as the skip-gram model with negative sampling of Mikolov et al. We conduct experiments on a language network, a social network as well as a citation network and show that our learned global representations can be effectively used as features in tasks such as clustering, classification and visualization. Empirical results demonstrate that our representation significantly outperforms other state-of-the-art methods in such tasks

References in zbMATH (referenced in 14 articles )

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  1. Han, Xinyu; Zhao, Yi; Small, Michael: Revisiting the memory capacity in reservoir computing of directed acyclic network (2021)
  2. 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)
  3. 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
  4. Ananyeva, Marina; Makarov, Ilya; Pendiukhov, Mikhail: GSM: inductive learning on dynamic graph embeddings (2020)
  5. Benedek Rozemberczki, Oliver Kiss, Rik Sarkar: An API Oriented Open-source Python Framework for Unsupervised Learning on Graphs (2020) arXiv
  6. Chunaev, Petr: Community detection in node-attributed social networks: a survey (2020)
  7. Li, Bentian; Pi, Dechang; Lin, Yunxia; Khan, Izhar Ahmed; Cui, Lin: Multi-source information fusion based heterogeneous network embedding (2020)
  8. Lv, Shaoqing; Xiang, Ju; Feng, Jingyu; Wang, Honggang; Lu, Guangyue; Li, Min: Community enhancement network embedding based on edge reweighting preprocessing (2020)
  9. Nie, Binling; Sun, Shouqian: Context-dependent representation of knowledge graphs (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. William L. Hamilton, Rex Ying, Jure Leskovec: Inductive Representation Learning on Large Graphs (2017) arXiv
  14. Yang, Ning; He, Lifang; Li, Zheng; Yu, Philip S.: Reducing uncertainty of dynamic heterogeneous information networks: a fusing reconstructing approach (2017)