A decentralised approach for link inference in large signed graphs

Abstract Social networks as large graphs have interesting information embedded within. The presence of links between nodes characterises the underlying relationships between nodes. Link inference is an interesting problem and has been studied for unsigned and signed graphs. Whilst the signed links give more insight into the node relationships, the class imbalance and the limited availability of signed graph datasets obstructs the studies in this domain. Furthermore, the studies in literature usually consider a single large graph and ignore the underlying potentially different sub-graphs in the original graph. In this work, we consider signed graphs for link inference with a focus on negative links and adopt a decentralised approach to learn the graph and sub-graph embeddings, i.e., we consider sub-graphs of the original signed graph for link inference. As we focus on negative links, the problem becomes more challenging due to the class-imbalance and sparsity of the sub-graphs. For the input graph, we employ a decentralised approach to learn the latent factors in the sub-graphs using probabilistic matrix factorisation. We perform an extensive experimental study using real datasets to assess the applicability and effectiveness of the approach. The results show that the decentralised approach is a promising consideration and gives encouraging results for the performance and scalability of the solution.

[1]  Dragomir R. Radev,et al.  Extracting Signed Social Networks from Text , 2012, TextGraphs@ACL.

[3]  W. Art Chaovalitwongse,et al.  A novel link prediction approach for scale-free networks , 2014, WWW.

[4]  Alexander J. Smola,et al.  Friend or frenemy?: predicting signed ties in social networks , 2012, SIGIR '12.

[5]  Zhi-Hua Zhou,et al.  Exploratory Under-Sampling for Class-Imbalance Learning , 2006, Sixth International Conference on Data Mining (ICDM'06).

[6]  Charu C. Aggarwal,et al.  Signed Network Embedding in Social Media , 2017, SDM.

[7]  Jianping Fan,et al.  On spatio-temporal blockchain query processing , 2019, Future Gener. Comput. Syst..

[8]  Lei Li,et al.  Recognizing sentiment of relations between entities in text , 2014 .

[9]  Junghwan Kim,et al.  SIDE: Representation Learning in Signed Directed Networks , 2018, WWW.

[10]  Ali Hassan Sodhro,et al.  A multi-sensor data fusion enabled ensemble approach for medical data from body sensor networks , 2020, Inf. Fusion.

[11]  Feng Liu,et al.  Deep Belief Network-Based Approaches for Link Prediction in Signed Social Networks , 2015, Entropy.

[12]  Christos Faloutsos,et al.  Edge Weight Prediction in Weighted Signed Networks , 2016, 2016 IEEE 16th International Conference on Data Mining (ICDM).

[13]  Rajeev Raman,et al.  Uncertainty in Sequential Pattern Mining , 2010, BNCOD.

[14]  Ruslan Salakhutdinov,et al.  Bayesian probabilistic matrix factorization using Markov chain Monte Carlo , 2008, ICML '08.

[15]  Jure Leskovec,et al.  Supervised random walks: predicting and recommending links in social networks , 2010, WSDM '11.

[16]  Mohammad S. Obaidat,et al.  A decentralised approach to privacy preserving trajectory mining , 2020, Future Gener. Comput. Syst..

[17]  Nagarajan Natarajan,et al.  Exploiting longer cycles for link prediction in signed networks , 2011, CIKM '11.

[18]  Nicola Barbieri,et al.  Who to follow and why: link prediction with explanations , 2014, KDD.

[19]  Alper Ozcan,et al.  Multivariate Time Series Link Prediction for Evolving Heterogeneous Network , 2019, Int. J. Inf. Technol. Decis. Mak..

[20]  Jiliang Tang,et al.  Signed Graph Convolutional Network , 2018, ArXiv.

[21]  Fan Zhang,et al.  Spatio-Temporal Analysis of Passenger Travel Patterns in Massive Smart Card Data , 2017, IEEE Transactions on Intelligent Transportation Systems.

[22]  Christian S. Jensen,et al.  Efficient Online Summarization of Large-Scale Dynamic Networks , 2016, IEEE Transactions on Knowledge and Data Engineering.

[23]  Mawloud Omar,et al.  A robust trust inference algorithm in weighted signed social networks based on collaborative filtering and agreement as a similarity metric , 2019, J. Netw. Comput. Appl..

[24]  Sho Yokoi,et al.  Link Prediction in Sparse Networks by Incidence Matrix Factorization , 2017, J. Inf. Process..

[25]  Panagiotis Symeonidis,et al.  Transitive node similarity: predicting and recommending links in signed social networks , 2014, World Wide Web.

[26]  Inderjit S. Dhillon,et al.  Low rank modeling of signed networks , 2012, KDD.

[27]  Charu C. Aggarwal,et al.  Negative Link Prediction in Social Media , 2014, WSDM.

[28]  Nagarajan Natarajan,et al.  Prediction and clustering in signed networks: a local to global perspective , 2013, J. Mach. Learn. Res..

[29]  Qing Ling,et al.  Distributed stochastic gradient descent for link prediction in signed social networks , 2019, EURASIP Journal on Advances in Signal Processing.

[30]  Jure Leskovec,et al.  Signed networks in social media , 2010, CHI.

[31]  Gediminas Adomavicius,et al.  Toward the next generation of recommender systems: a survey of the state-of-the-art and possible extensions , 2005, IEEE Transactions on Knowledge and Data Engineering.

[32]  Philip S. Yu,et al.  Mining top-K large structural patterns in a massive network , 2011, Proc. VLDB Endow..

[33]  Jérôme Kunegis,et al.  Learning spectral graph transformations for link prediction , 2009, ICML '09.

[34]  Charu C. Aggarwal,et al.  An Ensemble Approach to Link Prediction , 2017, IEEE Transactions on Knowledge and Data Engineering.

[35]  Rami Puzis,et al.  Link Prediction in Social Networks Using Computationally Efficient Topological Features , 2011, 2011 IEEE Third Int'l Conference on Privacy, Security, Risk and Trust and 2011 IEEE Third Int'l Conference on Social Computing.

[36]  Nagiza F. Samatova,et al.  Practical Graph Mining with R , 2013 .

[37]  David Liben-Nowell,et al.  The link-prediction problem for social networks , 2007 .

[38]  Hugo D. Lopes,et al.  Calibration of Machine Learning Classifiers for Probability of Default Modelling , 2017, 1710.08901.

[39]  Bo Sun,et al.  A robust multi-class AdaBoost algorithm for mislabeled noisy data , 2016, Knowl. Based Syst..