Paper Info
Title
Graph-Based Friend Recommendation In Social Networks Using Artificial Bee Colony
Abstract
Friend recommendation is a fundamental problem in online social networks, which aims to recommend new links for each user. In this paper, a new methodology based on graph topology and artificial bee colony is proposed to effective friend recommendation in social networks. In proposed approach, a sub-graph of network is composed by the study user and all the other connected users separately by three degree of separation from the root user. The proposed recommendation system computes four parameters within the generated sub-graph, and suggests the new links for the root user. Artificial bee colony is applied to optimize the relative importance of the weights of each parameter. To verify the proposed methodology, we chose a graph with 1000 members from YouTube. We considered the 20% of all links within the network graph to learning the system using artificial bee colony algorithm. These links were removed from the graph, and a data was generated by using all candidate nodes within the resulted graph, to be a recommend. Then, the generated data were divided into training set and evaluation set. Obtained results demonstrated the robustness of proposed approach with a 36% return rate.
Year
DOI
Venue
2013
10.1109/DASC.2013.108
2013 IEEE 11TH INTERNATIONAL CONFERENCE ON DEPENDABLE, AUTONOMIC AND SECURE COMPUTING (DASC)
Keywords
Field
DocType
artificial bee colony, genetic algorithm, online social networks, friend recommendation, friends-of-friends
Recommender system,Graph,Artificial bee colony algorithm,Six degrees of separation,Social network,Computer science,Robustness (computer science),Artificial intelligence,Topological graph theory,Machine learning,Genetic algorithm
Conference
Citations 
PageRank 
References 
2
0.40
9
Authors
3
Name
Order
Citations
PageRank
Fatemeh Akbari120.40
Amir Hooshang Tajfar220.40
Akbar Farhoodi Nejad320.40