The link prediction problem for social networks
CIKM '03 Proceedings of the twelfth international conference on Information and knowledge management
Email is a stage: discovering people roles from email archives
Proceedings of the 27th annual international ACM SIGIR conference on Research and development in information retrieval
Probability Estimates for Multi-class Classification by Pairwise Coupling
The Journal of Machine Learning Research
The dynamics of viral marketing
ACM Transactions on the Web (TWEB)
A note on Platt's probabilistic outputs for support vector machines
Machine Learning
Yes, there is a correlation: - from social networks to personal behavior on the web
Proceedings of the 17th international conference on World Wide Web
Proceedings of the 18th international conference on World wide web
Social influence analysis in large-scale networks
Proceedings of the 15th ACM SIGKDD international conference on Knowledge discovery and data mining
Topic and role discovery in social networks with experiments on enron and academic email
Journal of Artificial Intelligence Research
You are who you know: inferring user profiles in online social networks
Proceedings of the third ACM international conference on Web search and data mining
Inferring relevant social networks from interpersonal communication
Proceedings of the 19th international conference on World wide web
Probabilistic Graphical Models: Principles and Techniques - Adaptive Computation and Machine Learning
Networks, Crowds, and Markets: Reasoning About a Highly Connected World
Networks, Crowds, and Markets: Reasoning About a Highly Connected World
Supervised random walks: predicting and recommending links in social networks
Proceedings of the fourth ACM international conference on Web search and data mining
Finding social roles in Wikipedia
Proceedings of the 2011 iConference
Who says what to whom on twitter
Proceedings of the 20th international conference on World wide web
Social Network Data Analytics
Proceedings of the 12th ACM conference on Electronic commerce
Outlier detection in graph streams
ICDE '11 Proceedings of the 2011 IEEE 27th International Conference on Data Engineering
User-level sentiment analysis incorporating social networks
Proceedings of the 17th ACM SIGKDD international conference on Knowledge discovery and data mining
Positive and Unlabeled Learning for Graph Classification
ICDM '11 Proceedings of the 2011 IEEE 11th International Conference on Data Mining
Inferring social ties across heterogenous networks
Proceedings of the fifth ACM international conference on Web search and data mining
Social status and role analysis of palin's email network
Proceedings of the 21st international conference companion on World Wide Web
On Text Clustering with Side Information
ICDE '12 Proceedings of the 2012 IEEE 28th International Conference on Data Engineering
Magnet community identification on social networks
Proceedings of the 18th ACM SIGKDD international conference on Knowledge discovery and data mining
RolX: structural role extraction & mining in large graphs
Proceedings of the 18th ACM SIGKDD international conference on Knowledge discovery and data mining
Anatomy of a web-scale resale market: a data mining approach
Proceedings of the 22nd international conference on World Wide Web
Inferring the impacts of social media on crowdfunding
Proceedings of the 7th ACM international conference on Web search and data mining
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Users in online social networks play a variety of social roles and statuses. For example, users in Twitter can be represented as advertiser, content contributor, information receiver, etc; users in Linkedin can be in different professional roles, such as engineer, salesperson and recruiter. Previous research work mainly focuses on using categorical and textual information to predict the attributes of users. However, it cannot be applied to a large number of users in real social networks, since much of such information is missing, outdated and non-standard. In this paper, we investigate the social roles and statuses that people act in online social networks in the perspective of network structures, since the uniqueness of social networks is connecting people. We quantitatively analyze a number of key social principles and theories that correlate with social roles and statuses. We systematically study how the network characteristics reflect the social situations of users in an online society. We discover patterns of homophily, the tendency of users to connect with users with similar social roles and statuses. In addition, we observe that different factors in social theories influence the social role/status of an individual user to various extent, since these social principles represent different aspects of the network. We then introduce an optimization framework based on Factor Conditioning Symmetry, and we propose a probabilistic model to integrate the optimization framework on local structural information as well as network influence to infer the unknown social roles and statuses of online users. We will present experiment results to show the effectiveness of the inference.