Fibonacci heaps and their uses in improved network optimization algorithms
Journal of the ACM (JACM)
A Faster Katz Status Score Algorithm
Computational & Mathematical Organization Theory
SimRank: a measure of structural-context similarity
Proceedings of the eighth ACM SIGKDD international conference on Knowledge discovery and data mining
The link prediction problem for social networks
CIKM '03 Proceedings of the twelfth international conference on Information and knowledge management
Automatic multimedia cross-modal correlation discovery
Proceedings of the tenth ACM SIGKDD international conference on Knowledge discovery and data mining
An Experimental Investigation of Graph Kernels on a Collaborative Recommendation Task
ICDM '06 Proceedings of the Sixth International Conference on Data Mining
Fast Random Walk with Restart and Its Applications
ICDM '06 Proceedings of the Sixth International Conference on Data Mining
IEEE Transactions on Knowledge and Data Engineering
MapReduce: simplified data processing on large clusters
Communications of the ACM - 50th anniversary issue: 1958 - 2008
Make new friends, but keep the old: recommending people on social networking sites
Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
Social search in "Small-World" experiments
Proceedings of the 18th international conference on World wide web
Folks in Folksonomies: social link prediction from shared metadata
Proceedings of the third ACM international conference on Web search and data mining
Towards time-aware link prediction in evolving social networks
Proceedings of the 3rd Workshop on Social Network Mining and Analysis
Predicting positive and negative links in online social networks
Proceedings of the 19th international conference on World wide web
Transitive node similarity for link prediction in social networks with positive and negative links
Proceedings of the fourth ACM conference on Recommender systems
Using friendship ties and family circles for link prediction
SNAKDD'08 Proceedings of the Second international conference on Advances in social network mining and analysis
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Online social networks (OSNs) recommend new friends to registered users based on local-based features of the graph (i.e. based on the number of common friends that two users share). However, OSNs do not exploit all different length paths of the network. Instead, they consider only pathways of maximum length 2 between a user and his candidate friends. On the other hand, there are global-based approaches, which detect the overall path structure in a network, being computationally prohibitive for huge-sized social networks. In this paper we provide friend recommendations, also known as the link prediction problem, by traversing all paths of a limited length, based on the ''algorithmic small world hypothesis''. As a result, we are able to provide more accurate and faster friend recommendations. We also derive variants of our method that apply to different types of networks (directed/undirected and signed/unsigned). We perform an extensive experimental comparison of the proposed method against existing link prediction algorithms, using synthetic and three real data sets (Epinions, Facebook and Hi5). We also show that a significant accuracy improvement can be gained by using information about both positive and negative edges. Finally, we discuss extensively various experimental considerations, such as a possible MapReduce implementation of FriendLink algorithm to achieve scalability.