Link prediction and path analysis using Markov chains
Proceedings of the 9th international World Wide Web conference on Computer networks : the international journal of computer and telecommunications netowrking
Introduction to Modern Information Retrieval
Introduction to Modern Information Retrieval
Using Markov Chains for Link Prediction in Adaptive Web Sites
Soft-Ware 2002 Proceedings of the First International Conference on Computing in an Imperfect World
ACM SIGKDD Explorations Newsletter
The link-prediction problem for social networks
Journal of the American Society for Information Science and Technology
Link Prediction of Social Networks Based on Weighted Proximity Measures
WI '07 Proceedings of the IEEE/WIC/ACM International Conference on Web Intelligence
Combining Collective Classification and Link Prediction
ICDMW '07 Proceedings of the Seventh IEEE International Conference on Data Mining Workshops
Using ghost edges for classification in sparsely labeled networks
Proceedings of the 14th ACM SIGKDD international conference on Knowledge discovery and data mining
Local Probabilistic Models for Link Prediction
ICDM '07 Proceedings of the 2007 Seventh IEEE International Conference on Data Mining
Improving learning in networked data by combining explicit and mined links
AAAI'07 Proceedings of the 22nd national conference on Artificial intelligence - Volume 1
Bridging the gap: complex networks meet information and knowledge management
Proceedings of the 18th ACM conference on Information and knowledge management
Centrality prediction in dynamic human contact networks
Computer Networks: The International Journal of Computer and Telecommunications Networking
Friendship prediction and homophily in social media
ACM Transactions on the Web (TWEB)
Group and link analysis of multi-relational scientific social networks
Journal of Systems and Software
| Hi-index | 0.00 |
Plenty of algorithms for link prediction have been proposed and were applied to various real networks. Among these works, the weights of links are rarely taken into account. In this paper, we use local similarity indices to estimate the likelihood of the existence of links in weighted networks, including Common Neighbor, Adamic-Adar Index, Resource Allocation Index, and their weighted versions. In both the unweighted and weighted cases, the resource allocation index performs the best. To our surprise, the weighted indices perform worse, which reminds us of the well-known Weak Tie Theory. Further experimental study shows that the weak ties play a significant role in the link prediction problem, and to emphasize the contribution of weak ties can remarkably enhance the predicting accuracy.