Open Access Open Access  Restricted Access Subscription or Fee Access

Model for Link Prediction in Social Network by Genetic Algorithm Approach

Yachana Bhawsar, Dr. G. S. Thakur, Dr. R.S. Thakur

Abstract


Social networking sites are increasing their features day by day to gain the attention of users. There are lots of research works in this field. Out of many research areas this paper focuses on link prediction using soft computing technique. We used various features of social network and applied genetic algorithm to predict links. Selection of features to build chromosome is main task in genetic algorithm. Number of runs will get different chromosomes i.e. shown in results. Normalization of features is also done depending upon their priority. Results show that with the increase in dataset size chances of correct prediction increases.

Keywords


Social Network, Link Prediction, Genetic Algorithm

Full Text:

PDF

References


A. Popescul, L. Ungar, S. Lawrence, and D. Pennock, “Statistical relational learning for document mining,” in ICDM, 2003.

B. Taskar, P. Abbeel, and D. Kollerk, “Discriminative probabilistic models for relational data,” in UAI, 2002.

C. Wang, V. Satuluri, and S. Parthasarathy, “Local probabilistic models for link prediction,” in ICDM, 2007.

D. Liben-Nowell and J. Kleinberg, “The link prediction problem for social networks,” in LinkKDD, 2004.

H. Kashima and N. Abe, “A parameterized probabilistic model of evolution for supervised link prediction,” in ICDM, 2006.

L. Getoor, N. Friedman, D. Koller, and B. Taskar, “Learning probabilistic models of relational structure,” in ICML, 2001.

Lada A. Adamic and Eytan Adar,” Friends and neighbors on the web”, Social Networks, 25(3):211-230, July 2003.

M. Al-Hassan, V. Chaoji, S. Salem, and M. J. Zaki, “Link prediction using supervised learning,” in workshop on Link Analysis, Counterterrorism and Security (at SDM), 2005.

M. Bilgic, G. Namata, and L. Getoor, “Combining collective classification and link prediction,” in Workshop on Mining Graphs and Complex Structures (at ICDM), 2007.

M. E. J. Newman, “Clustering and preferential attachment in growing networks,” Physical review Letters, 2001.

O. Hassanzadeh and et al, “A framework for semantic link discovery over relational data,” in CIKM, 2009.

O. Nasraoui and R. Krishnapuram, “One step evolutionary mining of context sensitive associations and Web navigation patterns,” in Proc. SIAM Int. Conf. Data Mining, Arlinton, VA, Apr. 2002, pp, 531-547.

Barbasi Albert-Laszlo, Albert Reka, Emergence of scaling in random networks, Science, 509-512, 2009.

Gong z. et l., co-fermentation of cellobiose and xylose by Lipomyces starkeyi for lipid production. Bioresour Technol. 117, 20-24, 2012.

Clauset A., Shalizi C. R., and Newman M. E. J., Power-law distributions in empirical data, SIAM Review, 51, 661-703, 2009.

Li, R. H. at el. Link prediction: the power of maximal entropy random walk.Proceedings of the 20th ACM International Conference on Information and Knowledge Management (CIKM) 1147–1156. ACM, New York, 2011.

Lictenwalter, Lussier, et al., New perspectives and methods in link prediction. Proceedings of the 16th ACM SIGKDD international conference on Knowledge discovery and data mining. 243-252. ACM, New York, 2010.

Goldberg, D.E.: Genetic Algorithms in Search, Optimization and Machine Learning. Addison-Wesley Publishing (1989).

Holland, J.H.: Adaptation in Natural and Artificial Systems. Univ. of Michigan Press, Ann Harbor Mich. (1975)


Refbacks

  • There are currently no refbacks.


Creative Commons License
This work is licensed under a Creative Commons Attribution 3.0 License.