Bipartite Link Prediction based on Topological Features via 2-hop Path

A variety of real-world systems can be modeled as bipartite networks. One of the most powerful and simple link prediction methods is Linear-Graph Autoencoder(LGAE) which has promising performance on challenging tasks such as link prediction and node clustering. LGAE relies on simple linear model w.r.t. the adjacency matrix of the graph to learn vector space representations of nodes. In this paper, we consider the case of bipartite link predictions where node attributes are unavailable. When using LGAE, we propose to multiply the reconstructed adjacency matrix with a symmetrically normalized training adjacency matrix. As a result, 2-hop paths are formed which we use as the predicted adjacency matrix to evaluate the performance of our model. Experimental results on both synthetic and real-world dataset show our approach consistently outperforms Graph Autoencoder and Linear Graph Autoencoder model in 10 out of 12 bipartite dataset and reaches competitive performances in 2 other bipartite dataset.

[1]  Stefano Ermon,et al.  Graphite: Iterative Generative Modeling of Graphs , 2018, ICML.

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

[3]  Michalis Vazirgiannis,et al.  A Degeneracy Framework for Scalable Graph Autoencoders , 2019, IJCAI.

[4]  Pengfei Jiao,et al.  Similarity-based Regularized Latent Feature Model for Link Prediction in Bipartite Networks , 2017, Scientific Reports.

[5]  Simone Daminelli,et al.  Common neighbours and the local-community-paradigm for topological link prediction in bipartite networks , 2015, ArXiv.

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

[7]  Yi-Cheng Zhang,et al.  Bipartite network projection and personal recommendation. , 2007, Physical review. E, Statistical, nonlinear, and soft matter physics.

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

[9]  Robert Frederking,et al.  RWR-GAE: Random Walk Regularization for Graph Auto Encoders , 2019, ArXiv.

[10]  Lina Yao,et al.  Adversarially Regularized Graph Autoencoder , 2018, IJCAI.

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

[12]  Michalis Vazirgiannis,et al.  Keep It Simple: Graph Autoencoders Without Graph Convolutional Networks , 2019, ArXiv.

[13]  Jure Leskovec,et al.  Representation Learning on Graphs: Methods and Applications , 2017, IEEE Data Eng. Bull..

[14]  Philip S. Yu,et al.  A Comprehensive Survey on Graph Neural Networks , 2019, IEEE Transactions on Neural Networks and Learning Systems.

[15]  Burleigh B. Gardner,et al.  Deep South: A Social Anthropological Study of Caste and Class , 1942 .

[16]  Max Welling,et al.  Graph Convolutional Matrix Completion , 2017, ArXiv.

[17]  Max Welling,et al.  Semi-Supervised Classification with Graph Convolutional Networks , 2016, ICLR.

[18]  Wilfried Philips,et al.  Matrix Completion with Variational Graph Autoencoders: Application in Hyperlocal Air Quality Inference , 2018, ICASSP 2019 - 2019 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP).

[19]  Cyrus Shahabi,et al.  Bipartite Graph Neural Networks for Efficient Node Representation Learning , 2019 .

[20]  Céline Rouveirol,et al.  Supervised Machine Learning Applied to Link Prediction in Bipartite Social Networks , 2010, 2010 International Conference on Advances in Social Networks Analysis and Mining.

[21]  Yoshihiro Yamanishi,et al.  Prediction of drug–target interaction networks from the integration of chemical and genomic spaces , 2008, ISMB.

[22]  Chun Wang,et al.  MGAE: Marginalized Graph Autoencoder for Graph Clustering , 2017, CIKM.

[23]  Fei Li,et al.  Predicting drug side effects based on link prediction in bipartite network , 2014, 2014 7th International Conference on Biomedical Engineering and Informatics.

[24]  Sahin Albayrak,et al.  The Link Prediction Problem in Bipartite Networks , 2010, IPMU.

[25]  Yoshihiro Yamanishi,et al.  DINIES: drug–target interaction network inference engine based on supervised analysis , 2014, Nucleic Acids Res..