Abstract
Link prediction requires predicting which new links are likely to appear in a graph. In this paper, we present an approach for link prediction that relies on higher-order analysis of the graph topology, well beyond the typical approach which relies on common neighbors. We treat the link prediction problem as a supervised classification problem, and we propose a set of features that depend on the patterns or motifs that a pair of nodes occurs in. By using motifs of sizes 3, 4, and 5, our approach captures a high level of detail about the graph topology. In addition, we propose two optimizations to construct the classification dataset from the graph. First, we propose adding negative examples to the graph as an alternative to the common approach of removing positive ones. Second, we show that it is important to control for the shortest-path distance when sampling pairs of nodes to form negative examples, since the difficulty of prediction varies with the distance. We experimentally demonstrate that using our proposed motif features in off-the-shelf classifiers results in up to 10% points increase in accuracy over prior topology-based and feature-learning methods.
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Notes
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Code available at https://github.com/GhadeerAbuoda/LinkPrediction.
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References
Abuoda, G., De Francisci Morales, G., Aboulnaga, A.: Link prediction via higher-order motif features. arXiv preprint arXiv:1902.06679 (2019)
Adamic, L.A., Adar, E.: Friends and neighbors on the web. Soc. Netw. 25(3), 211–230 (2003)
Ahmed, N.K., Neville, J., Rossi, R.A., Duffield, N.: Efficient graphlet counting for large networks. In: ICDM, pp. 1–10 (2015)
Aiello, L.M., Barrat, A., Schifanella, R., Cattuto, C., Markines, B., Menczer, F.: Friendship prediction and homophily in social media. TWEB 6(2), 9 (2012)
Airoldi, E.M., Blei, D.M., Fienberg, S.E., Xing, E.P., Jaakkola, T.: Mixed membership stochastic block models for relational data with application to protein-protein interactions. In: International Biometrics Society Annual Meeting, vol. 15 (2006)
Al Hasan, M., Chaoji, V., Salem, S., Zaki, M.: Link prediction using supervised learning. In: Workshop on Link Analysis, Counter-Terrorism and Security (2006)
Al Hasan, M., Zaki, M.J.: A survey of link prediction in social networks. In: Aggarwal, C. (ed.) Social Network Data Analytics, pp. 243–275. Springer, Boston (2011). https://doi.org/10.1007/978-1-4419-8462-3_9
Barabási, A.L.: Scale-free networks: a decade and beyond. Science 325(5939), 412–413 (2009)
Bressan, M., Chierichetti, F., Kumar, R., Leucci, S., Panconesi, A.: Counting graphlets: space vs. time. In: WSDM, pp. 557–566 (2017)
Chen, H., Li, X., Huang, Z.: Link prediction approach to collaborative filtering. In: JCDL, pp. 141–142 (2005)
Cukierski, W., Hamner, B., Yang, B.: Graph-based features for supervised link prediction. In: IJCNN (2011)
Fire, M., Tenenboim, L., Lesser, O., Puzis, R., Rokach, L., Elovici, Y.: Link prediction in social networks using computationally efficient topological features. In: Proceedings of the International Conference on Privacy, Security, Risk and Trust (PASSAT) (2011)
Folino, F., Pizzuti, C.: Link prediction approaches for disease networks. In: Proceedings of the International Conference on Information Technology in Bio and Medical Informatics (2012)
Gao, F., Musial, K., Cooper, C., Tsoka, S.: Link prediction methods and their accuracy for different social networks and network metrics. Sci. Program. 2015, 1 (2015)
Getoor, L., Diehl, C.P.: Link mining: a survey. SIGKDD Explor. Newsl. 7(2), 3–12 (2005)
Grover, A., Leskovec, J.: node2vec: scalable feature learning for networks. In: KDD (2016)
Hulovatyy, Y., Solava, R.W., Milenković, T.: Revealing missing parts of the interactome via link prediction. PLOS ONE (2014)
Juszczyszyn, K., Kazienko, P., Musiał, K.: Local topology of social network based on motif analysis. In: Lovrek, I., Howlett, R.J., Jain, L.C. (eds.) KES 2008. LNCS (LNAI), vol. 5178, pp. 97–105. Springer, Heidelberg (2008). https://doi.org/10.1007/978-3-540-85565-1_13
Juszczyszyn, K., Musial, K., Budka, M.: Link prediction based on subgraph evolution in dynamic social networks. In: SocialCom (2011)
Katz, L.: A new status index derived from sociometric analysis. Psychometrika 18(1), 39–43 (1953)
Kipf, T.N., Welling, M.: Semi-supervised classification with graph convolutional networks. arXiv preprint arXiv:1609.02907 (2016)
Lee, J.B., Rossi, R.A., Kong, X., Kim, S., Koh, E., Rao, A.: Higher-order graph convolutional networks. arXiv preprint arXiv:1809.07697 (2018)
Leskovec, J., Huttenlocher, D., Kleinberg, J.: Predicting positive and negative links in online social networks. In: WWW (2010)
Liben-Nowell, D., Kleinberg, J.: The link-prediction problem for social networks. J. Assoc. Inf. Sci. Technol. 58(7), 1019–1031 (2007)
Lichtenwalter, R.N., Lussier, J.T., Chawla, N.V.: New perspectives and methods in link prediction. In: KDD, pp. 243–252 (2010)
Lu, L., Zhou, T.: Link prediction in complex networks: a survey. arXiv preprint arXiv:1010.0725 (2010)
Menon, A.K., Elkan, C.: Link prediction via matrix factorization. In: Gunopulos, D., Hofmann, T., Malerba, D., Vazirgiannis, M. (eds.) ECML PKDD 2011. LNCS (LNAI), vol. 6912, pp. 437–452. Springer, Heidelberg (2011). https://doi.org/10.1007/978-3-642-23783-6_28
Milo, R., et al.: Superfamilies of evolved and designed networks. Science 303(5663), 1538–1542 (2004)
Milo, R., Shen-Orr, S., Itzkovitz, S., Kashtan, N., Chklovskii, D., Alon, U.: Network motifs: simple building blocks of complex networks. Science 298(5594), 824–827 (2002)
Newman, M.E.: Clustering and preferential attachment in growing networks. Phys. Rev. E 64(2), 025102 (2001)
Palla, G., Derényi, I., Farkas, I., Vicsek, T.: Uncovering the overlapping community structure of complex networks in nature and society. Nature 435(7043), 814 (2005)
Rahman, M., Hasan, M.A.: Link prediction in dynamic networks using graphlet. In: Frasconi, P., Landwehr, N., Manco, G., Vreeken, J. (eds.) ECML PKDD 2016. LNCS (LNAI), vol. 9851, pp. 394–409. Springer, Cham (2016). https://doi.org/10.1007/978-3-319-46128-1_25
Rossi, R.A., Ahmed, N.K., Koh, E.: Higher-order network representation learning. In: Companion Proceedings of the Web Conference (WWW), pp. 3–4 (2018)
Rossi, R.A., Ahmed, N.K., Koh, E., Kim, S., Rao, A., Yadkori, Y.A.: HONE: higher-order network embeddings. arXiv preprint arXiv:1801.09303 (2018)
Sa, H.R., Prudencio, R.B.: Supervised learning for link prediction in weighted networks. In: Proceedings International Workshop on Web and Text Intelligence (2010)
Schlichtkrull, M., Kipf, T.N., Bloem, P., van den Berg, R., Titov, I., Welling, M.: Modeling relational data with graph convolutional networks. In: Gangemi, A., et al. (eds.) ESWC 2018. LNCS, vol. 10843, pp. 593–607. Springer, Cham (2018). https://doi.org/10.1007/978-3-319-93417-4_38
Schneider, D.S., Hudson, K.L., Lin, T.Y., Anderson, K.V.: Dominant and recessive mutations define functional domains of Toll, a transmembrane protein required for dorsal-ventral polarity in the Drosophila embryo. Genes Dev. 5(5), 797–807 (1991)
Shen-Orr, S.S., Milo, R., Mangan, S., Alon, U.: Network motifs in the transcriptional regulation network of Escherichia coli. Nat. Genet. 31(1), 64 (2002)
Soutoglou, E., Talianidis, I.: Coordination of PIC assembly and chromatin remodeling during differentiation-induced gene activation. Science 295(5561), 1901–1904 (2002)
Tang, J., Qu, M., Wang, M., Zhang, M., Yan, J., Mei, Q.: LINE: large-scale information network embedding. In: WWW, pp. 1067–1077 (2015)
Teixeira, C.H., Fonseca, A.J., Serafini, M., Siganos, G., Zaki, M.J., Aboulnaga, A.: Arabesque: a system for distributed graph mining. In: SOSP, pp. 425–440 (2015)
Tsitsulin, A., Mottin, D., Karras, P., Müller, E.: VERSE: versatile graph embeddings from similarity measures. In: WWW (2018)
Vazquez, A., Dobrin, R., Sergi, D., Eckmann, J.P., Oltvai, Z., Barabási, A.L.: The topological relationship between the large-scale attributes and local interaction patterns of complex networks. Proc. Natl. Acad. Sci. 101(52), 17940–17945 (2004)
Yang, Y., Lichtenwalter, R.N., Chawla, N.V.: Evaluating link prediction methods. Knowl. Inf. Syst. 45(3), 751–782 (2014). https://doi.org/10.1007/s10115-014-0789-0
Zhang, M., Chen, Y.: Link prediction based on graph neural networks. In: NeurIPS, pp. 5171–5181 (2018)
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Abuoda, G., De Francisci Morales, G., Aboulnaga, A. (2020). Link Prediction via Higher-Order Motif Features. In: Brefeld, U., Fromont, E., Hotho, A., Knobbe, A., Maathuis, M., Robardet, C. (eds) Machine Learning and Knowledge Discovery in Databases. ECML PKDD 2019. Lecture Notes in Computer Science(), vol 11906. Springer, Cham. https://doi.org/10.1007/978-3-030-46150-8_25
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