The link prediction problem for social networks
CIKM '03 Proceedings of the twelfth international conference on Information and knowledge management
ACM SIGKDD Explorations Newsletter
Fast Random Walk with Restart and Its Applications
ICDM '06 Proceedings of the Sixth International Conference on Data Mining
Social link recommendation by learning hidden topics
Proceedings of the fifth ACM conference on Recommender systems
Structural link analysis and prediction in microblogs
Proceedings of the 20th ACM international conference on Information and knowledge management
Who will follow whom? exploiting semantics for link prediction in attention-information networks
ISWC'12 Proceedings of the 11th international conference on The Semantic Web - Volume Part I
curso: protect yourself from curse of attribute inference: a social network privacy-analyzer
Proceedings of the ACM SIGMOD Workshop on Databases and Social Networks
Link prediction in social networks based on hypergraph
Proceedings of the 22nd international conference on World Wide Web companion
Solving the missing node problem using structure and attribute information
Proceedings of the 2013 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining
SocialSearch+: enriching social network with web evidences
World Wide Web
Who proposed the relationship?: recovering the hidden directions of undirected social networks
Proceedings of the 23rd international conference on World wide web
| Hi-index | 0.00 |
With the phenomenal success of networking sites (e.g., Facebook, Twitter and LinkedIn), social networks have drawn substantial attention. On online social networking sites, link recommendation is a critical task that not only helps improve user experience but also plays an essential role in network growth. In this paper we propose several link recommendation criteria, based on both user attributes and graph structure. To discover the candidates that satisfy these criteria, link relevance is estimated using a random walk algorithm on an augmented social graph with both attribute and structure information. The global and local influence of the attributes is leveraged in the framework as well. Besides link recommendation, our framework can also rank attributes in a social network. Experiments on DBLP and IMDB data sets demonstrate that our method outperforms state-of-the-art methods based on network structure and node attribute information for link recommendation.