Link prediction algorithm based on attention mechanism

被引：0
|
作者：
Cheng H. [1 ]
Zhang L. [1 ]
Fang Y. [1 ]
机构：
[1] College of Information Science and Engineering, East China University of Science and Technology, Shanghai
来源：
Huazhong Keji Daxue Xuebao (Ziran Kexue Ban)/Journal of Huazhong University of Science and Technology (Natural Science Edition) | 2019年 / 47卷 / 02期
关键词：
Attention mechanism; Bi-directional recurrent neural network (Bi-RNN); Link prediction; Local network; Topological serialization;
D O I：
10.13245/j.hust.190220
中图分类号：
学科分类号：
摘要：
Aiming at the link prediction task in social networks, a link representation method and a link prediction algorithm based on attention mechanism were proposed. A link local network was designed which contained a node pair to be link predicted and their co-neighbors, and was serialized by multiple closely walks. Links sequences were encoded by bi-directional recurrent neural network(Bi-RNN) for its ability on mining the context information from the sequential nodes. The attention mechanism, used to focus and weight the nodes in the link, strengthens the contribution of important nodes, which can promote the accuracy of link prediction. Experiments on four types of social network datasets show that the algorithm has significant improvement in accuracy and computational efficiency, and is suitable for multiple social networks. © 2019, Editorial Board of Journal of Huazhong University of Science and Technology. All right reserved.
引用
收藏
页码：109 / 114
页数：5
相关论文
共 14 条
  • [1] Liben-Nowell D., Kleinberg J., The link prediction problem for social networks, Journal of the Association for Information Science and Technology, 58, 7, pp. 1019-1031, (2007)
  • [2] Lichtenwalter R.N., Lussier J.T., Chawla N.V., New perspectives and methods in link prediction, Proc of the 16th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 243-252, (2010)
  • [3] Hasan M., Mohammad A., Link prediction using supervised learning, Workshop on Link Analysis Counterterrorism and Security, 30, 9, pp. 798-805, (2006)
  • [4] Li K., Gao J., Guo S., Et al., LRBM: arestricted boltzmann machine based approach for representation learning on linked data, Proc of IEEE International Conference on Data Mining, pp. 300-309, (2015)
  • [5] Das R., Neelakantan A., Bela Nger D., Et al., Chains of reasoning over entities, relations, and text using recurrent neuralnetworks
  • [6] Xiong W., Hoang T., Wang W.Y., Deeppath: a reinforcement learning method for knowledge graph reasoning, Proc of Conference on Empirical Methods in Natural Language Processing, pp. 564-573, (2017)
  • [7] Schuster M., Paliwal K.K., Bidirectional recurrent neural networks, IEEE Transactions on Signal Processing, 45, 11, pp. 2673-2681, (1997)
  • [8] Newma M.E., Clustering and preferential attachment in growing networks, Physical Review E: Statistical Nonlinear and Soft Matter Physics, 64, 2, pp. 1-4, (2001)
  • [9] Zhou T., Lyu L.Y., Zhang Y.C., Predicting missing links via local information, The European Physical Journal: B, 71, 4, pp. 623-630, (2009)
  • [10] Adamic L.A., Adar E., Friends and neighbors on the web, Social Networks, 25, 3, pp. 211-230, (2003)
12