High-order Link Prediction Method Based on Motif Aggregation Coefficient and Time Series Division

被引：0
|
作者：
Kang Z.-G. [1 ]
Jin F.-S. [1 ]
Wang G.-R. [1 ]
机构：
[1] School of Computer Science and Technology, Beijing Institute of Technology, Beijing
来源：
Ruan Jian Xue Bao/Journal of Software | 2021年 / 32卷 / 03期
基金：
国家重点研发计划; 中国国家自然科学基金;
关键词：
Dynamic networks; Graph based machine learning; High-order network structure; Link prediction;
D O I：
10.13328/j.cnki.jos.006172
中图分类号：
学科分类号：
摘要：
High-level link prediction is a hot and difficult problem in network analysis research. An excellent high-level link prediction algorithm can not only mine the potential relationship between nodes in a complex network but also help to understand the law of network structure evolves over time. Exploring unknown network relationships has important applications. Most traditional link prediction algorithms only consider the structural similarity between nodes, while ignoring the characteristics of higher-order structures and information about network changes. This study proposes a high-order link prediction model based on Motif clustering coefficients and time series partitioning (MTLP). This model constructs a representational feature vector by extracting the features of Motif clustering coefficients and network structure evolution of high-order structures in the network, and uses multilayer perceptron (MLP) network model to complete the link prediction task. By conducting experiments on different real-life data sets, the results show that the proposed MTLP model has better high-order link prediction performance than the state-of-the-art methods. © Copyright 2021, Institute of Software, the Chinese Academy of Sciences. All rights reserved.
引用
收藏
页码：712 / 725
页数：13
相关论文
共 28 条
  • [1] Liben-Nowell D, Kleinberg J., The link-prediction problem for social networks, Journal of the American Society for Information Science and Technology, 58, 7, pp. 1019-1031, (2007)
  • [2] Backstrom L, Leskovec J., Supervised random walks: Predicting and recommending links in social networks, Proc. of the 4th ACM Int'l Conf. on Web Search and Data Mining, pp. 635-644, (2011)
  • [3] Wang X, Gulbahce N, Yu H., Network-Based methods for human disease gene prediction, Briefings in Functional Genomics, 10, 5, pp. 280-293, (2011)
  • [4] Tang J, Wu S, Sun J, Et al., Cross-domain collaboration recommendation, Proc. of the 18th ACM SIGKDD Int'l Conf. on Knowledge Discovery and Data Mining, pp. 1285-1293, (2012)
  • [5] Benson AR, Gleich DF, Leskovec J., Higher-order organization of complex networks, Science, 353, 6295, pp. 163-166, (2016)
  • [6] Newman MEJ., Clustering and preferential attachment in growing networks, Physical Review E, 64, 2, (2001)
  • [7] Adamic LA, Adar E., Friends and neighbors on the Web, Social Networks, 25, 3, pp. 211-230, (2003)
  • [8] Barabasi AL, Jeong H, Neda Z, Et al., Evolution of the social network of scientific collaborations, Physica A: Statistical Mechanics and Its Applications, 311, 3-4, pp. 590-614, (2002)
  • [9] Katz L., A new status index derived from sociometric analysis, Psychometrika, 18, 1, pp. 39-43, (1953)
  • [10] Wang C, Satuluri V, Parthasarathy S., Local probabilistic models for link prediction, Proc. of the 7th IEEE Int'l Conf. on Data Mining (ICDM 2007), pp. 322-331, (2007)
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