Stochastic link and group detection
Eighteenth national conference on Artificial intelligence
The link prediction problem for social networks
CIKM '03 Proceedings of the twelfth international conference on Information and knowledge management
The case for anomalous link discovery
ACM SIGKDD Explorations Newsletter
Finding tribes: identifying close-knit individuals from employment patterns
Proceedings of the 13th ACM SIGKDD international conference on Knowledge discovery and data mining
SNAKDD 2008 social network mining and analysis postworkshop report
ACM SIGKDD Explorations Newsletter
Learning algorithms for link prediction based on chance constraints
ECML PKDD'10 Proceedings of the 2010 European conference on Machine learning and knowledge discovery in databases: Part I
The impact of network structure on breaking ties in online social networks: unfollowing on twitter
Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
Private discovery of common social contacts
ACNS'11 Proceedings of the 9th international conference on Applied cryptography and network security
Towards trust inference from bipartite social networks
DBSocial '12 Proceedings of the 2nd ACM SIGMOD Workshop on Databases and Social Networks
Fast and accurate link prediction in social networking systems
Journal of Systems and Software
Predicting group evolution in the social network
SocInfo'12 Proceedings of the 4th international conference on Social Informatics
Transforming graph data for statistical relational learning
Journal of Artificial Intelligence Research
Acquaintance or partner?: predicting partnership in online and location-based social networks
Proceedings of the 2013 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining
Different approaches to community evolution prediction in blogosphere
Proceedings of the 2013 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining
Network flows and the link prediction problem
Proceedings of the 7th Workshop on Social Network Mining and Analysis
Analyzing future communities in growing citation networks
Proceedings of the 2013 international workshop on Mining unstructured big data using natural language processing
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Social networks can capture a variety of relationships among the participants. Both friendship and family ties are commonly studied, but most existing work studies them in isolation. Here, we investigate how these networks can be overlaid, and propose a feature taxonomy for link prediction. We show that when there are tightly-knit family circles in a social network, we can improve the accuracy of link prediction models. This is done by making use of the family circle features based on the likely structural equivalence of family members. We investigated the predictive power of overlaying friendship and family ties on three real-world social networks. Our experiments demonstrate significantly higher prediction accuracy (between 15% and 30% more accurate) compared to using more traditional features such as descriptive node attributes and structural features. The experiments also show that a combination of all three types of attributes results in the best precision-recall trade-off.