Abstract
The semantic approach to data linked in social networks uses information extracted from node attributes to quantify the similarity between nodes. In contrast, the topological approach exploits the structural information of the network, e.g., nodes degree, paths, neighbourhood breadth. For a long time, such approaches have been considered substantially separated. In recent years, following the widespread of social media, an increasing focus has been dedicated to understanding how complex networks develop, following the human phenomena they represent, considering both the meaning of the node and the links structure and distribution. The link prediction problem, aiming at predicting how networks evolve in terms of connections between entities, is suitable to apply semantic similarity measures to a topological domain. In this paper, we introduce a novel topological formulation of semantic measures, e.g., NGD, PMI, Confidence, in a unifying framework for link prediction in social graphs, providing results of systematic experiments. We validate the approach discussing the prediction capability on widely accepted data sets, comparing the performance of the topological formulation of semantic measures to the conventional metrics generally used in literature.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Adamic, L.A., Adar, E.: Friends and neighbors on the web. Soc. Netw. 25(3), 211–230 (2003). https://doi.org/10.1016/S0378-8733(03)00009-1
Agrawal, R., Imieliundefinedski, T., Swami, A.: Mining association rules between sets of items in large databases. In: Proceedings of the 1993 ACM SIGMOD International Conference on Management of Data, SIGMOD 1993, pp. 207–216. Association for Computing Machinery, New York (1993). https://doi.org/10.1145/170035.170072
Chiancone, A., Franzoni, V., Li, Y., Markov, K., Milani, A.: Leveraging zero tail in neighbourhood for link prediction, pp. 135–139 (2016). https://doi.org/10.1109/WI-IAT.2015.129
Chiancone, A., Franzoni, V., Niyogi, R., Milani, A.: Improving link ranking quality by quasi-common neighbourhood, pp. 21–26 (2015). https://doi.org/10.1109/ICCSA.2015.19
Church, K.W., Hanks, P.: Word association noms, mutual information, and lexicography. In: Proceedings of the 27th Annual Conference of the Association for Computational Linguistics, vol. 16, no. 1, pp. 22–29 (1989). https://doi.org/10.3115/981623.981633
Cilibrasi, R., Vitanyi, P.: The google similarity distance, arxiv.org or clustering by compression. IEEE J. Trans. Inf. Theory 51(4), 1523–1545 (2004)
Franzoni, V., Chiancone, A., Milani, A.: A multistrain bacterial diffusion model for link prediction. Int. J. Pattern Recogn. Artif. Intell. 31(11) (2017). https://doi.org/10.1142/S0218001417590248
Franzoni, V., Milani, A.: PMING distance: a collaborative semantic proximity measure, vol. 2, pp. 442–449 (2012). https://doi.org/10.1109/WI-IAT.2012.226
Franzoni, V., Milani, A.: Structural and semantic proximity in information networks. In: Gervasi, O., et al. (eds.) ICCSA 2017. LNCS, vol. 10404, pp. 651–666. Springer, Cham (2017). https://doi.org/10.1007/978-3-319-62392-4_47
Franzoni, V., Milani, A., Pallottelli, S., Leung, C., Li, Y.: Context-based image semantic similarity, pp. 1280–1284 (2016). https://doi.org/10.1109/FSKD.2015.7382127
Franzoni, V.: Misure di prossimità semantica per il Web. Master’s thesis (2012)
Franzoni, V.: A unifiying approach to semantic and topological similarity in information networks. Ph.D. thesis (2017)
Franzoni, V., Milani, A.: PMING distance: a collaborative semantic proximity measure. In: Proceedings - 2012 IEEE/WIC/ACM International Conference on Intelligent Agent Technology, IAT 2012, vol. 2, pp. 442–449 (2012). https://doi.org/10.1109/WI-IAT.2012.226
Franzoni, V., Milani, A.: Heuristic semantic walk. In: Murgante, B., et al. (eds.) ICCSA 2013. LNCS, vol. 7974, pp. 643–656. Springer, Heidelberg (2013). https://doi.org/10.1007/978-3-642-39649-6_46
Jaccard, P.: Étude comparative de la distribution florale dans une portion des Alpes et des Jura. Bulletin del la Société Vaudoise des Sciences Naturelles 37(JANUARY 1901), 547–579 (1901). https://doi.org/10.5169/seals-266450
Kunegis, J.: Konect: the koblenz network collection. In: Proceedings of the 22nd International Conference on World Wide Web, WWW 2013, Companion, pp. 1343–1350. Association for Computing Machinery, New York (2013). https://doi.org/10.1145/2487788.2488173
Leskovec, J., Kleinberg, J., Faloutsos, C.: Graph evolution: densification and shrinking diameters. ACM Trans. Knowl. Discov. Data 1(1), 2-es (2007). https://doi.org/10.1145/1217299.1217301
Leskovec, J., Krevl, A.: SNAP Datasets: Stanford large network dataset collection, June 2014. http://snap.stanford.edu/data
Liben-Nowell, D., Kleinberg, J.: The link-prediction problem for social networks. J. Am. Soc. Inform. Sci. Technol. 58(7), 1019–1031 (2007). https://doi.org/10.1002/asi.20591
Manning, C.D., Schütze, H., Weikurn, G.: Foundations of statistical natural language processing. SIGMOD Rec. (2002). https://doi.org/10.1145/601858.601867
McAuley, J., Leskovec, J.: Learning to discover social circles in ego networks. In: Proceedings of the 25th International Conference on Neural Information Processing Systems, NIPS 2012, vol. 1, pp. 539–547. Curran Associates Inc., Red Hook (2012)
Michalski, R., Palus, S., Kazienko, P.: Matching organizational structure and social network extracted from email communication. In: Abramowicz, W. (ed.) BIS 2011. LNBIP, vol. 87, pp. 197–206. Springer, Heidelberg (2011). https://doi.org/10.1007/978-3-642-21863-7_17
Newman, M.E.J.: Finding community structure in networks using the eigenvectors of matrices, May 2006. https://doi.org/10.1103/PhysRevE.74.036104
Rossi, R.A., Ahmed, N.K.: The network data repository with interactive graph analytics and visualization. In: AAAI (2015). http://networkrepository.com
Rozemberczki, B., Sarkar, R.: Characteristic functions on graphs: birds of a feather, from statistical descriptors to parametric models (2020)
Turney, P.D.: Mining the web for synonyms: PMI-IR versus LSA on TOEFL. In: De Raedt, L., Flach, P. (eds.) ECML 2001. LNCS (LNAI), vol. 2167, pp. 491–502. Springer, Heidelberg (2001). https://doi.org/10.1007/3-540-44795-4_42
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
Yin, H., Benson, A.R., Leskovec, J., Gleich, D.F.: Local higher-order graph clustering. In: Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, KDD 2017, pp. 555–564. Association for Computing Machinery, New York (2017). https://doi.org/10.1145/3097983.3098069
Zhou, T., Lu, L., Zhang, Y.C.: Predicting missing links via local information. Eur. Phys. J. B 71(4), 623–630 (2009). https://doi.org/10.1140/epjb/e2009-00335-8
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2020 Springer Nature Switzerland AG
About this paper
Cite this paper
Biondi, G., Franzoni, V. (2020). Semantic Similarity Measures for Topological Link Prediction. In: Gervasi, O., et al. Computational Science and Its Applications – ICCSA 2020. ICCSA 2020. Lecture Notes in Computer Science(), vol 12253. Springer, Cham. https://doi.org/10.1007/978-3-030-58814-4_10
Download citation
DOI: https://doi.org/10.1007/978-3-030-58814-4_10
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-58813-7
Online ISBN: 978-3-030-58814-4
eBook Packages: Computer ScienceComputer Science (R0)