Temporal Link Prediction Using Node Centrality and Time Series

Link prediction is an important task in the area of complex networks. Some networks can be better modeled by temporal networks where the patterns of link appearance and disappearance varying with time. However, most of the previous link prediction researches ignore the temporal behaviors of links. The temporal link prediction needs to predict future links via a known network, considering the temporal relationship of node pairs. We propose a method combining the node centrality with time series. We distinguish the contributions of common neighbors to link generation by their centralities. Compared with benchmark approaches in several temporal networks, the proposed method can improve the accuracy of temporal link prediction efficiently.

[1]  Alneu de Andrade Lopes,et al.  Exploiting behaviors of communities of twitter users for link prediction , 2013, Social Network Analysis and Mining.

[2]  Linyuan Lü,et al.  Predicting missing links via local information , 2009, 0901.0553.

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

[4]  Padhraic Smyth,et al.  Prediction and ranking algorithms for event-based network data , 2005, SKDD.

[5]  Ricardo B. C. Prudêncio,et al.  Time Series Based Link Prediction , 2012, The 2012 International Joint Conference on Neural Networks (IJCNN).

[6]  Przemyslaw Kazienko,et al.  Matching Organizational Structure and Social Network Extracted from Email Communication , 2011, BIS.

[7]  Tom Fawcett,et al.  An introduction to ROC analysis , 2006, Pattern Recognit. Lett..

[8]  Hui Tian,et al.  Link prediction based on node centrality , 2018, ICITEE2018.

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

[10]  Julie Fournet,et al.  Data on face-to-face contacts in an office building suggest a low-cost vaccination strategy based on community linkers , 2014, Network Science.

[11]  Linyuan Lu,et al.  Link Prediction in Complex Networks: A Survey , 2010, ArXiv.

[12]  Lise Getoor,et al.  Link mining: a survey , 2005, SKDD.

[13]  Hui Tian,et al.  Hidden link prediction based on node centrality and weak ties , 2013 .

[14]  Jonathan L. Herlocker,et al.  Evaluating collaborative filtering recommender systems , 2004, TOIS.

[15]  Linyuan Lu,et al.  Role of weak ties in link prediction of complex networks , 2009, CIKM-CNIKM.

[16]  Jon M. Kleinberg,et al.  The link-prediction problem for social networks , 2007, J. Assoc. Inf. Sci. Technol..

[17]  Ryan A. Rossi,et al.  The Network Data Repository with Interactive Graph Analytics and Visualization , 2015, AAAI.

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

[19]  Cecilia Mascolo,et al.  A multilayer approach to multiplexity and link prediction in online geo-social networks , 2016, EPJ Data Science.

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

[21]  Bhaskar Biswas,et al.  Community-based link prediction , 2017, Multimedia Tools and Applications.

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

[23]  Zan Huang,et al.  The Time-Series Link Prediction Problem with Applications in Communication Surveillance , 2009, INFORMS J. Comput..