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Identifying Most Relevant Node Path To Increase Connection Probability In Graph Network
Abhiram Gandhe, Parag Deshpande
Pages - 1 - 8     |    Revised - 01-03-2015     |    Published - 31-03-2015
Published in International Journal of Data Engineering (IJDE)
Volume - 6   Issue - 1    |    Publication Date - March 2015  Table of Contents
MORE INFORMATION
References   |   Abstracting & Indexing
KEYWORDS
Friendship Link, Online Social Network, Graph Network, Node Path, Reference Chain.
ABSTRACT
In social networks, one of the most challenging problems is to find the best way to establish a relationship between two nodes. Different attributes (Topological, Non-Topological) can be used to define friendship score between two nodes which indicates the strength of a relationship. NonTopological attributes can be used to define the strength of a relationship even if two nodes are not connected. The concept of friendship score to define the strength of a relationship between two nodes transforms social network into a complete graph where each node is connected to every other node and where friendship score is used as link attribute. The information on already existing connections in social media network and graph which is formed based on friendship score can be used to find out best way of connecting two different nodes even if no path is in existence in social media network between these nodes.

In this paper, we propose a novel way of estimating friendship score using non-topological attributes based on available information in social media network and algorithm to find out best way of connecting two nodes in the form of chain of reference. The chain of reference between node X1 and Xn is a path X1->X2->….->Xn-1->Xn where each link Xi->Xj is having high friendship score. The chain of reference indicates how X1 can be connected to Xn even if no path exists between X1 and Xn in social media network.
ABSTRACTING & INDEXING
1 Google Scholar 
2 CiteSeerX 
3 refSeek 
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6 PdfSR 
REFERENCES
A. Gandhe, P. Deshpande. Use of Non-Topological Node Attribute values for Probabilistic Determination of Link Formation. International Journal of Advanced Computer Science and Applications(IJACSA), Volume 6 Issue 2, 2015, PP. 186-191.
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L. Katz. A new status index derived from sociometric analysis. Psychometrika, 1953, pp. 39 43.
M. Fire, L. Tenenboim, O. Lesser, R. Puzis, L. Rokach, Y. Elovici. Link Prediction in Social Networks using Computationally Efficient Topological Features. IEEE International Conference on Privacy, Security, Risk, and Trust, and IEEE International Conference on Social Computing, 2011.
S. Milgram. The small world problem. PsychologyToday, 1967, pp. 61–67.
S.Wasserman and K. Faust. Social network analysis: Methods and applications. 1994.
MANUSCRIPT AUTHORS
Mr. Abhiram Gandhe
Computer Science and Engineering Visvesvaraya National Institute of Technology Nagpur, India - India
abhiram.gandhe@gmail.com
Dr. Parag Deshpande
Computer Science and Engineering Visvesvaraya National Institute of Technology Nagpur, India - India


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