Abstract
As we know due to advancement in technology it is very easy to be in connection with others. People interact with each other and they create, share and exchange information and ideas. Social network is one of the most attracted areas in recent years. Link prediction is a key research area. In our proposed method we study link prediction using heuristic approach. Most of the previous papers only considered the network topology, they didn’t consider the nodes properties individually, and they treated them only as passive entity in graph and using only network properties. But in our proposed method we will consider different parameter of nodes that define the behavior of nodes and one important issue that we will consider in our method is “New researchers” because they are willing to get help in identifying potential collaborators. Thus our focus will also on “New researchers” and we would also like to have a quantitative analysis of the performance of the different existing methods and to study some domain specific heuristics that would improve the degree of prediction.
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References
Liben Nowell, D., Kleinberg, J.: The link prediction problem for social networks. In: Proceedings of the Twelfth International Conference on Information and Knowledge Mangement, CIKM 2003 (2003)
Newman, M.E.J.: Clustering and preferential attachment in growing networks. Physics Review E (2001)
Saramaki, J., Kaski, K.: Scale-free networks generated by random walkers. Physica A 341 (2004)
Jeong, H., Neda, Z., Barabasi, A.-L.: Measuring preferential attachment for evolving networks. Europhysics Letters (2003)
Adamic, L.A., Adar, E.: Friends and neighbors on the web (2003)
Fire, M., Tenenboim, L.: Link prediction in social networks using computationally efficient Topological feature. In: 2011 IEEE International Conference on Privacy, Security Risk (2011)
Sachan, M., Ichise, R.: Using Semantic information to improve Link prediction results in network datasets. IACSIT International Journal of Engineering and Technology (2010)
Wohlfarth, T., Ichise, R.: Semantic and Event-Based Approach for Link Prediction. In: Yamaguchi, T. (ed.) PAKM 2008. LNCS (LNAI), vol. 5345, pp. 50–61. Springer, Heidelberg (2008)
Pavlov, M., Ichise, R.: Finding experts by link prediction in co authorship networks. In: Proceeding of the 2nd International Workshop on Finding Expert on the Web with Semantics (2007)
Pallavi, R.N.: A Heuristic based technique for inferring links in Social Network. In: International Conference on Recent Advances in Engineering and Technology (ICRAET 2012), Hyderabad, India, April 29-30 (2012)
Katz, J.S.: Geographical proximity and scientific collaboration. Scientometrics (1994)
Melin, G., Persson, O.: Studying research collaboration using co- authorship. Scientometrics (1996)
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Panwar, A.P.S., Niyogi, R. (2015). A Heuristic for Link Prediction in Online Social Network. In: Buyya, R., Thampi, S. (eds) Intelligent Distributed Computing. Advances in Intelligent Systems and Computing, vol 321. Springer, Cham. https://doi.org/10.1007/978-3-319-11227-5_4
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DOI: https://doi.org/10.1007/978-3-319-11227-5_4
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-11226-8
Online ISBN: 978-3-319-11227-5
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