Abstract
A graph path, a sequence of continuous edges in a graph, is one of the most important objects used in many studies of link prediction in social networks. It is integrated in measures, which can be used to quantify the relationship between two nodes. Due to the small-world hypothesis, using short paths with bounded lengths, called local paths, nearly preserves information, but reduces computational complexity compared to the overall paths in social networks. In this paper, we exploit local paths, particularly paths with weight, for the link-prediction problem. We use PropFlow [16], which computes information flow between nodes based on local paths, to evaluate a relationship between two nodes. The higher the PropFlow, the higher the probability that the nodes will connect in the future. In this measure, link strength has a strong link to the measure’s performance as it directs information flow. Therefore, we investigate ways of building a model that can efficiently combine more than one useful property into link strength so that it can improve the performance of PropFlow.
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References
Baker, J.E.: Reducing Bias and Inefficiency in The Selection Algorithm. In: Proceedings of the Second International Conference on Genetic Algorithms (1987)
Viswanath, B., Mislove, A., Cha, M., Gummadi, K.P.: On the Evolution of User Interaction in Facebook. In: Proceedings of the 2nd ACM Workshop on Online Social Networks, pp. 37–42 (2009)
Thi, D.B., Hoang, T.-A.N.: Features Extraction for Link Prediction in Social Networks. In: 13th International Conference on IEEE Computational Science and Its Applications (ICCSA) (2013)
Eric, G., Karahalios, K.: Predicting Tie Strength with Social Media. In: Proceedings of the SIGCHI Conference on Human Factors in Computing Systems (2009)
Fire, M., Tenenboim, L., Lesser, O., Puzis, R., Rokach, L., Elovici, Y.: Link Prediction in Social Networks Using Computationally Efficient Topological Features. In: Privacy, Security, Risk and Trust (PASSAT), 2011 IEEE Third International Conference on Social Computing (SOCIALCOM), pp. 73–80 (2011)
Granovetter, M.: The Strength of Weak Ties. American Journal of Sociology 78(6), 1360–1380 (1973)
Japkowicz, N., Stephen, S.: The Class Imbalance Problem: A Systematic Study. Journal Intelligent Data Analysis 6(5), 429–449 (2002)
Ye, J., Cheng, H., Zhu, Z., Chen, M.: Predicting Positive and Negative Links in Signed Social Networks by Transfer Learning. In: Proceedings of the 22nd International Conference on World Wide Web, pp. 1477–1488 (2011)
Indika, K., Neville, J.: Using Transactional Information to Predict Link Strength in Online Social Networks. In: ICWSM (2009)
Juszczyszyn, K., Musial, K., Budka, M.: Link Prediction Based on Subgraph Evolution in Dynamic Social Networks. In: Privacy, Security, Risk and Trust IEEE 3rd International Conference (2011)
Backstrom, L., Leskovec, J.: Supervised Random Walks: Predicting and Recommending Links in Social Networks. In: Proceeding of the 4th ACM International Conference on Web Search and Data Mining, pp. 635–644 (2011)
Jure, L., Huttenlocher, D., Kleinberg, J.: Predicting Positive and Negative Links in Online Social Networks. In: Proceedings of The 19th International Conference on World Wide Web. ACM (2010)
Liben-Nowell, D., Kleinberg, J.: The Link Prediction Problem for Social Networks. Journal of the American Society for Information Science and Technology 58(7), 1019–1031 (2007)
Lankeshwara, M., Ichise, R.: Link Prediction in Social Networks using Information Flow via Active Links. IEICE Transactions on Information and Systems 96(7), 1495–1502 (2013)
Papadimitriou, A., Symeonidis, P., Manolopoulos, Y.: Scalable Link Prediction in Social Networks based on Local Graph Characteristics. In: 9th International Conference on Information Technology: New Generations (ITNG) (2012)
Lichtenwalter, R.N., Lussier, J.T., Chawla, N.V.: New Perspectives and Methods in Link Prediction. In: Proceedings of the 16th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (2010)
Mrinmaya, S., Ichise, R.: Using Semantic Information to Improve Link Prediction Results in Network Datasets. International Journal of Computer Theory and Engineering 3, 71–76 (2011)
Scellato, S., Noulas, A., Mascolo, C.: Exploiting Place Features in Link Prediction on Location-based Social Networks. In: Proceedings of the 17th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 1046–1054 (2011)
Rongjing, X., Neville, J., Rogati, M.: Modeling relationship strength in online social networks. In: Proceedings of the 19th International Conference on World Wide Web. ACM (2010)
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Thi, D.B., Ichise, R., Le, B. (2014). Link Prediction in Social Networks Based on Local Weighted Paths. In: Dang, T.K., Wagner, R., Neuhold, E., Takizawa, M., Küng, J., Thoai, N. (eds) Future Data and Security Engineering. FDSE 2014. Lecture Notes in Computer Science, vol 8860. Springer, Cham. https://doi.org/10.1007/978-3-319-12778-1_12
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DOI: https://doi.org/10.1007/978-3-319-12778-1_12
Publisher Name: Springer, Cham
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