MODEL: Motif-Based Deep Feature Learning for Link Prediction

Link prediction plays an important role in network analysis and applications. Recently, approaches for link prediction have evolved from traditional similarity-based algorithms into embedding-based algorithms. However, most existing approaches fail to exploit the fact that real-world networks are different from random networks. In particular, real-world networks are known to contain motifs, natural network building blocks reflecting the underlying network-generating processes. In this article, we propose a novel embedding algorithm that incorporates network motifs to capture higher order structures in the network. To evaluate its effectiveness for link prediction, experiments were conducted on three types of networks: social networks, biological networks, and academic networks. The results demonstrate that our algorithm outperforms both the traditional similarity-based algorithms (by 20%) and the state-of-the-art embedding-based algorithms (by 19%).

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

[2]  Omer Levy,et al.  Neural Word Embedding as Implicit Matrix Factorization , 2014, NIPS.

[3]  Gianmarco De Francisci Morales,et al.  Link Prediction via Higher-Order Motif Features , 2019, ECML/PKDD.

[4]  M. McPherson,et al.  Birds of a Feather: Homophily in Social Networks , 2001 .

[5]  Jure Leskovec,et al.  Higher-order organization of complex networks , 2016, Science.

[6]  M E J Newman,et al.  Community structure in social and biological networks , 2001, Proceedings of the National Academy of Sciences of the United States of America.

[7]  Jure Leskovec,et al.  Defining and evaluating network communities based on ground-truth , 2012, Knowledge and Information Systems.

[8]  Sebastian Wernicke,et al.  A Faster Algorithm for Detecting Network Motifs , 2005, WABI.

[9]  Feng Xia,et al.  Deep User Modeling for Content-based Event Recommendation in Event-based Social Networks , 2018, IEEE INFOCOM 2018 - IEEE Conference on Computer Communications.

[10]  Max Welling,et al.  Modeling Relational Data with Graph Convolutional Networks , 2017, ESWC.

[11]  Jeffrey Dean,et al.  Efficient Estimation of Word Representations in Vector Space , 2013, ICLR.

[12]  A. Arenas,et al.  Motif-based communities in complex networks , 2007, 0710.0059.

[13]  Chao Liu,et al.  Information Diffusion Nonlinear Dynamics Modeling and Evolution Analysis in Online Social Network Based on Emergency Events , 2019, IEEE Transactions on Computational Social Systems.

[14]  Jian Pei,et al.  A Survey on Network Embedding , 2017, IEEE Transactions on Knowledge and Data Engineering.

[15]  Guigang Zhang,et al.  Deep Learning , 2016, Int. J. Semantic Comput..

[16]  Jimmy Ba,et al.  Adam: A Method for Stochastic Optimization , 2014, ICLR.

[17]  Chun-Hsi Huang,et al.  Biological network motif detection: principles and practice , 2012, Briefings Bioinform..

[18]  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.

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

[20]  Qiongkai Xu,et al.  GraRep: Learning Graph Representations with Global Structural Information , 2015, CIKM.

[21]  A-L Barabási,et al.  Structure and tie strengths in mobile communication networks , 2006, Proceedings of the National Academy of Sciences.

[22]  Feng Xia,et al.  Mobility Dataset Generation for Vehicular Social Networks Based on Floating Car Data , 2018, IEEE Transactions on Vehicular Technology.

[23]  Xiaoou Tang,et al.  Image Super-Resolution Using Deep Convolutional Networks , 2014, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[24]  Ryan A. Rossi,et al.  Higher-order Network Representation Learning , 2018, WWW.

[25]  Xiangnan He,et al.  Attributed Social Network Embedding , 2017, IEEE Transactions on Knowledge and Data Engineering.

[26]  N. Latha,et al.  Personalized Recommendation Combining User Interest and Social Circle , 2015 .

[27]  Wei Luo,et al.  Learning Graph Representation via Frequent Subgraphs , 2018, SDM.

[28]  Zhiwu Lu,et al.  RUM: Network Representation Learning Using Motifs , 2019, 2019 IEEE 35th International Conference on Data Engineering (ICDE).

[29]  Feng Xia,et al.  A Survey of Measures for Network Motifs , 2019, IEEE Access.

[30]  Ryan A. Rossi,et al.  Deep Inductive Network Representation Learning , 2018, WWW.

[31]  Mingzhe Wang,et al.  LINE: Large-scale Information Network Embedding , 2015, WWW.

[32]  Qi Xuan,et al.  Link Weight Prediction Using Supervised Learning Methods and Its Application to Yelp Layered Network , 2018, IEEE Transactions on Knowledge and Data Engineering.

[33]  Pasquale De Meo,et al.  On Facebook, most ties are weak , 2012, Commun. ACM.

[34]  Yuan Zhang,et al.  Enhancing the Network Embedding Quality with Structural Similarity , 2017, CIKM.

[35]  Manoj Reddy Dareddy,et al.  motif2vec: Motif Aware Node Representation Learning for Heterogeneous Networks , 2019, 2019 IEEE International Conference on Big Data (Big Data).

[36]  Qi Xuan,et al.  Subgraph Networks With Application to Structural Feature Space Expansion , 2019, IEEE Transactions on Knowledge and Data Engineering.

[37]  Geoffrey E. Hinton,et al.  Reducing the Dimensionality of Data with Neural Networks , 2006, Science.

[38]  Xiang Lin,et al.  N2VSCDNNR: A Local Recommender System Based on Node2vec and Rich Information Network , 2019, IEEE Transactions on Computational Social Systems.

[39]  Wenwu Zhu,et al.  Structural Deep Network Embedding , 2016, KDD.

[40]  Palash Goyal,et al.  Graph Embedding Techniques, Applications, and Performance: A Survey , 2017, Knowl. Based Syst..

[41]  Steven Skiena,et al.  DeepWalk: online learning of social representations , 2014, KDD.

[42]  William Noble Grundy,et al.  Meta-MEME: motif-based hidden Markov models of protein families , 1997, Comput. Appl. Biosci..

[43]  Gilad Lerman,et al.  Encoding robust representation for graph generation , 2018, 2019 International Joint Conference on Neural Networks (IJCNN).

[44]  Daniel R. Figueiredo,et al.  struc2vec: Learning Node Representations from Structural Identity , 2017, KDD.

[45]  U. Alon.  Network motifs: theory and experimental approaches , 2007, Nature Reviews Genetics.

[46]  Christos Faloutsos,et al.  Graph evolution: Densification and shrinking diameters , 2006, TKDD.

[47]  Yu Liu,et al.  Multipath2vec: Predicting Pathogenic Genes via Heterogeneous Network Embedding , 2018, 2018 IEEE International Conference on Bioinformatics and Biomedicine (BIBM).

[48]  Svetha Venkatesh,et al.  Learning graph representation via frequent subgraphs , 2018 .

[49]  Feng Xia,et al.  Big Scholarly Data: A Survey , 2017, IEEE Transactions on Big Data.

[50]  Wenjie Li,et al.  Predictive Network Representation Learning for Link Prediction , 2017, SIGIR.

[51]  S. Shen-Orr,et al.  Network motifs: simple building blocks of complex networks. , 2002, Science.

[52]  Jure Leskovec,et al.  node2vec: Scalable Feature Learning for Networks , 2016, KDD.

[53]  Yee Whye Teh,et al.  A Fast Learning Algorithm for Deep Belief Nets , 2006, Neural Computation.

[54]  Jeffrey Dean,et al.  Distributed Representations of Words and Phrases and their Compositionality , 2013, NIPS.

[55]  Sebastian Wernicke,et al.  FANMOD: a tool for fast network motif detection , 2006, Bioinform..

[56]  Maosong Sun,et al.  A Unified Framework for Community Detection and Network Representation Learning , 2016, IEEE Transactions on Knowledge and Data Engineering.

[57]  Rossano Schifanella,et al.  Friendship prediction and homophily in social media , 2012, TWEB.