Conditional Random Fields: Probabilistic Models for Segmenting and Labeling Sequence Data
ICML '01 Proceedings of the Eighteenth International Conference on Machine Learning
Propagation of trust and distrust
Proceedings of the 13th international conference on World Wide Web
The link-prediction problem for social networks
Journal of the American Society for Information Science and Technology
ArnetMiner: extraction and mining of academic social networks
Proceedings of the 14th ACM SIGKDD international conference on Knowledge discovery and data mining
Cluestr: mobile social networking for enhanced group communication
Proceedings of the ACM 2009 international conference on Supporting group work
Social influence analysis in large-scale networks
Proceedings of the 15th ACM SIGKDD international conference on Knowledge discovery and data mining
Relationship identification for social network discovery
AAAI'07 Proceedings of the 22nd national conference on Artificial intelligence - Volume 1
Signed networks in social media
Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
Predicting positive and negative links in online social networks
Proceedings of the 19th international conference on World wide web
Networks, Crowds, and Markets: Reasoning About a Highly Connected World
Networks, Crowds, and Markets: Reasoning About a Highly Connected World
Mining advisor-advisee relationships from research publication networks
Proceedings of the 16th ACM SIGKDD international conference on Knowledge discovery and data mining
Suggesting friends using the implicit social graph
Proceedings of the 16th ACM SIGKDD international conference on Knowledge discovery and data mining
New perspectives and methods in link prediction
Proceedings of the 16th ACM SIGKDD international conference on Knowledge discovery and data mining
Supervised random walks: predicting and recommending links in social networks
Proceedings of the fourth ACM international conference on Web search and data mining
Who says what to whom on twitter
Proceedings of the 20th international conference on World wide web
Learning to infer social ties in large networks
ECML PKDD'11 Proceedings of the 2011 European conference on Machine learning and knowledge discovery in databases - Volume Part III
Who will follow you back?: reciprocal relationship prediction
Proceedings of the 20th ACM international conference on Information and knowledge management
Loopy belief propagation for approximate inference: an empirical study
UAI'99 Proceedings of the Fifteenth conference on Uncertainty in artificial intelligence
Cross-lingual knowledge linking across wiki knowledge bases
Proceedings of the 21st international conference on World Wide Web
PAKDD'12 Proceedings of the 16th Pacific-Asia conference on Advances in Knowledge Discovery and Data Mining - Volume Part II
Back-buy prediction based on TriFG
Proceedings of the ACM SIGKDD Workshop on Mining Data Semantics
Mining competitive relationships by learning across heterogeneous networks
Proceedings of the 21st ACM international conference on Information and knowledge management
Inferring geographic coincidence in ephemeral social networks
ECML PKDD'12 Proceedings of the 2012 European conference on Machine Learning and Knowledge Discovery in Databases - Volume Part II
Patent partner recommendation in enterprise social networks
Proceedings of the sixth ACM international conference on Web search and data mining
Inferring social roles and statuses in social networks
Proceedings of the 19th ACM SIGKDD international conference on Knowledge discovery and data mining
Unsupervised link prediction using aggregative statistics on heterogeneous social networks
Proceedings of the 19th ACM SIGKDD international conference on Knowledge discovery and data mining
Confluence: conformity influence in large social networks
Proceedings of the 19th ACM SIGKDD international conference on Knowledge discovery and data mining
SAE: social analytic engine for large networks
Proceedings of the 19th ACM SIGKDD international conference on Knowledge discovery and data mining
Mining structural hole spanners through information diffusion in social networks
Proceedings of the 22nd international conference on World Wide Web
Learning to predict reciprocity and triadic closure in social networks
ACM Transactions on Knowledge Discovery from Data (TKDD)
On the use of mobility data for discovery and description of social ties
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
Inferring anchor links across multiple heterogeneous social networks
Proceedings of the 22nd ACM international conference on Conference on information & knowledge management
Transferring heterogeneous links across location-based social networks
Proceedings of the 7th ACM international conference on Web search and data mining
Opportunistic IoT: Exploring the harmonious interaction between human and the internet of things
Journal of Network and Computer Applications
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 |
It is well known that different types of social ties have essentially different influence on people. However, users in online social networks rarely categorize their contacts into "family", "colleagues", or "classmates". While a bulk of research has focused on inferring particular types of relationships in a specific social network, few publications systematically study the generalization of the problem of inferring social ties over multiple heterogeneous networks. In this work, we develop a framework for classifying the type of social relationships by learning across heterogeneous networks. The framework incorporates social theories into a factor graph model, which effectively improves the accuracy of inferring the type of social relationships in a target network by borrowing knowledge from a different source network. Our empirical study on five different genres of networks validates the effectiveness of the proposed framework. For example, by leveraging information from a coauthor network with labeled advisor-advisee relationships, the proposed framework is able to obtain an F1-score of 90% (8-28% improvements over alternative methods) for inferring manager-subordinate relationships in an enterprise email network.