Link prediction of time-evolving network based on node ranking

Abstract Many real-world networks belong to the kind that evolves over time. So it is very meaningful and challenging to predict whether the link will occur in the network of future time. In this paper, both time-evolving scale-free (SF) network and real-world dynamic network are taken into consideration first and then two kinds of methods are respectively proposed for link prediction. Different from many existing similarity-based dynamic network link prediction methods, many of which adopt node-pair similarity such as common neighbors (CN), Adamic–Adar (AA), and so on, we measure the similarity between nodes from a new perspective. With further research into node ranking, some eigenvector-based methods, such as PageRank (PR), Cumulative Nomination (CuN) and so on, can compute the values of node importance which can be regarded as the stationary distribution of Markov chain for all nodes iteratively. Therefore, from a statistical point of view, the importance of a node is like the probability of attracting other nodes to connect with it and the derivative value of a node pair is like the probability of attracting each other. These node-ranking-based approaches are very novel in the field of link prediction in that few researches have paid enough attention to them before. In addition, an adaptively time series forecasting method is proposed in this paper, and it uses the historical similarity series to predict the future similarity between each node pair adaptively. Experimental results show that our proposed algorithms can predict the future links not only for the growing SF network but also for the dynamic networks in the real-world.

[1]  Mohammad Reza Meybodi,et al.  Link prediction based on temporal similarity metrics using continuous action set learning automata , 2016 .

[2]  Lada A. Adamic,et al.  Friends and neighbors on the Web , 2003, Soc. Networks.

[3]  Xavier Bresson,et al.  Convolutional Neural Networks on Graphs with Fast Localized Spectral Filtering , 2016, NIPS.

[4]  Leo Katz,et al.  A new status index derived from sociometric analysis , 1953 .

[5]  Max Welling,et al.  Variational Graph Auto-Encoders , 2016, ArXiv.

[6]  Mohammad Al Hasan,et al.  DyLink2Vec: Effective Feature Representation for Link Prediction in Dynamic Networks , 2018, ArXiv.

[7]  Min Yang,et al.  An Advanced Deep Generative Framework for Temporal Link Prediction in Dynamic Networks , 2020, IEEE Transactions on Cybernetics.

[8]  Michael Small,et al.  Evolving networks—Using past structure to predict the future , 2016 .

[9]  M. Newman Clustering and preferential attachment in growing networks. , 2001, Physical review. E, Statistical, nonlinear, and soft matter physics.

[10]  Qi Xuan,et al.  E-LSTM-D: A Deep Learning Framework for Dynamic Network Link Prediction , 2019, IEEE Transactions on Systems, Man, and Cybernetics: Systems.

[11]  Hui Li,et al.  A Deep Learning Approach to Link Prediction in Dynamic Networks , 2014, SDM.

[12]  J. Hanley,et al.  The meaning and use of the area under a receiver operating characteristic (ROC) curve. , 1982, Radiology.

[13]  P. Bonacich Factoring and weighting approaches to status scores and clique identification , 1972 .

[14]  Palash Goyal,et al.  dyngraph2vec: Capturing Network Dynamics using Dynamic Graph Representation Learning , 2018, Knowl. Based Syst..

[15]  Dietrich Rebholz-Schuhmann,et al.  Predicting links between tumor samples and genes using 2-Layered graph based diffusion approach , 2019, BMC Bioinformatics.

[16]  Jinyin Chen,et al.  GC-LSTM: Graph Convolution Embedded LSTM for Dynamic Link Prediction , 2018, ArXiv.

[17]  Marie-Claude Boily,et al.  Dynamical systems to define centrality in social networks , 2000, Soc. Networks.

[18]  Spyros Makridakis,et al.  Forecasting Methods for Management , 1989 .

[19]  A. E. Hoerl,et al.  Ridge regression: biased estimation for nonorthogonal problems , 2000 .

[20]  George Athanasopoulos,et al.  Forecasting: principles and practice , 2013 .

[21]  Albert,et al.  Emergence of scaling in random networks , 1999, Science.

[22]  Fang Liu,et al.  Prediction of missing links based on community relevance and ruler inference , 2016, Knowl. Based Syst..

[23]  Duanbing Chen,et al.  Identifying Influential Spreaders by Weighted LeaderRank , 2013, ArXiv.

[24]  Jianhang Liu,et al.  Similarity-based future common neighbors model for link prediction in complex networks , 2018, Scientific Reports.

[25]  Guisheng Yin,et al.  Link prediction in dynamic networks based on the attraction force between nodes , 2019, Knowl. Based Syst..

[26]  Tong Wang,et al.  Link Prediction in Evolving Networks Based on Popularity of Nodes , 2017, Scientific Reports.

[27]  R. Tibshirani Regression Shrinkage and Selection via the Lasso , 1996 .

[28]  X. Rong Li,et al.  A two-stage algorithm for network reconstruction , 2018, Appl. Soft Comput..

[29]  Jürgen Schmidhuber,et al.  Long Short-Term Memory , 1997, Neural Computation.

[30]  Roger Guimerà,et al.  Missing and spurious interactions and the reconstruction of complex networks , 2009, Proceedings of the National Academy of Sciences.

[31]  Mathieu d'Aquin,et al.  Heat diffusion approach for scientific impact analysis in social media , 2019, Social Network Analysis and Mining.

[32]  Linyuan Lü,et al.  Toward link predictability of complex networks , 2015, Proceedings of the National Academy of Sciences.

[33]  Srikanta J. Bedathur,et al.  Towards time-aware link prediction in evolving social networks , 2009, SNA-KDD '09.

[34]  Justin Zhan,et al.  Deep Learning for Link Prediction in Dynamic Networks Using Weak Estimators , 2018, IEEE Access.

[35]  Zehra Cataltepe,et al.  Link prediction using time series of neighborhood-based node similarity scores , 2015, Data Mining and Knowledge Discovery.

[36]  Christos Faloutsos,et al.  Fast Random Walk with Restart and Its Applications , 2006, Sixth International Conference on Data Mining (ICDM'06).

[37]  Steven C. Wheelwright,et al.  Forecasting Methods for Management@@@Interactive Forecasting: Univariate and Multivariate Methods@@@Business Forecasting@@@The Management of Sales Forecasting , 1978 .

[38]  Michael Mitzenmacher,et al.  A Brief History of Generative Models for Power Law and Lognormal Distributions , 2004, Internet Math..

[39]  Tore Opsahl Triadic closure in two-mode networks: Redefining the global and local clustering coefficients , 2013, Soc. Networks.

[40]  Alex Pentland,et al.  Reality mining: sensing complex social systems , 2006, Personal and Ubiquitous Computing.

[41]  Kai Han,et al.  A Supervised Learning Approach to Link Prediction in Dynamic Networks , 2018, WASA.

[42]  Yan Liu,et al.  DynGEM: Deep Embedding Method for Dynamic Graphs , 2018, ArXiv.

[43]  Gobinda G. Chowdhury,et al.  Introduction to Modern Information Retrieval , 1999 .

[44]  Qian Zhang,et al.  Analyses and applications of optimization methods for complex network reconstruction , 2020, Knowl. Based Syst..

[45]  Alfred O. Hero,et al.  Dynamic Stochastic Blockmodels for Time-Evolving Social Networks , 2014, IEEE Journal of Selected Topics in Signal Processing.

[46]  Michael I. Jordan,et al.  On Spectral Clustering: Analysis and an algorithm , 2001, NIPS.

[47]  Yang Jiao,et al.  Reconstruction of networks from one-step data by matching positions , 2018 .