The link prediction problem for social networks
CIKM '03 Proceedings of the twelfth international conference on Information and knowledge management
MetaFac: community discovery via relational hypergraph factorization
Proceedings of the 15th ACM SIGKDD international conference on Knowledge discovery and data mining
LINKREC: a unified framework for link recommendation with user attributes and graph structure
Proceedings of the 19th international conference on World wide web
Music recommendation by unified hypergraph: combining social media information and music content
Proceedings of the international conference on Multimedia
Hi-index | 0.00 |
In recent years, online social networks have undergone a significant growth and attracted much attention. In these online social networks, link prediction is a critical task that not only offers insights into the factors behind creation of individual social relationship but also plays an essential role in the whole network growth. In this paper, we propose a novel link prediction method based on hypergraph. In contrast with conventional methods that using ordinary graph, we model the social network as a hypergraph, which can fully capture all types of objects and either the pair wise or high-order relations among these objects in the network. Then the link prediction task is formulated as a ranking problem on this hypergraph. Experimental results on Sina-Weibo dataset have demonstrated the effectiveness of our methods.