Conditional Random Fields: Probabilistic Models for Segmenting and Labeling Sequence Data
ICML '01 Proceedings of the Eighteenth International Conference on Machine Learning
The link-prediction problem for social networks
Journal of the American Society for Information Science and Technology
Preferential behavior in online groups
WSDM '08 Proceedings of the 2008 International Conference on Web Search and Data Mining
Local Probabilistic Models for Link Prediction
ICDM '07 Proceedings of the 2007 Seventh IEEE International Conference on Data Mining
Why We Twitter: An Analysis of a Microblogging Community
Advances in Web Mining and Web Usage Analysis
Social influence analysis in large-scale networks
Proceedings of the 15th ACM SIGKDD international conference on Knowledge discovery and data mining
TwitterRank: finding topic-sensitive influential twitterers
Proceedings of the third ACM international conference on Web search and data mining
What is Twitter, a social network or a news media?
Proceedings of the 19th international conference on World wide web
Predicting positive and negative links in online social networks
Proceedings of the 19th international conference on World wide web
Earthquake shakes Twitter users: real-time event detection by social sensors
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
TwitterMonitor: trend detection over the twitter stream
Proceedings of the 2010 ACM SIGMOD International Conference on Management of data
Mining advisor-advisee relationships from research publication networks
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
Social action tracking via noise tolerant time-varying factor graphs
Proceedings of the 16th ACM SIGKDD international conference on Knowledge discovery and data mining
Understanding retweeting behaviors in social networks
CIKM '10 Proceedings of the 19th ACM international conference on Information and knowledge management
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
Detecting the structure of social networks using (α, β)-communities
WAW'11 Proceedings of the 8th international conference on Algorithms and models for the web graph
Loopy belief propagation for approximate inference: an empirical study
UAI'99 Proceedings of the Fifteenth conference on Uncertainty in artificial intelligence
A generalized mean field algorithm for variational inference in exponential families
UAI'03 Proceedings of the Nineteenth conference on Uncertainty in Artificial Intelligence
Inferring social ties across heterogenous networks
Proceedings of the fifth ACM international conference on Web search and data mining
Cross-lingual knowledge linking across wiki knowledge bases
Proceedings of the 21st international conference on World Wide Web
Back-buy prediction based on TriFG
Proceedings of the ACM SIGKDD Workshop on Mining Data Semantics
We love rock 'n' roll: analyzing and predicting friendship links in Last.fm
Proceedings of the 3rd Annual ACM Web Science Conference
Feature selection for link prediction
Proceedings of the 5th Ph.D. workshop on Information and knowledge
Mining competitive relationships by learning across heterogeneous networks
Proceedings of the 21st ACM international conference on Information and knowledge management
Tweet classification based on their lifetime duration
Proceedings of the 21st ACM international conference on Information and knowledge management
Evolution of social-attribute networks: measurements, modeling, and implications using google+
Proceedings of the 2012 ACM conference on Internet measurement conference
Patent partner recommendation in enterprise social networks
Proceedings of the sixth ACM international conference on Web search and data mining
Towards Twitter context summarization with user influence models
Proceedings of the sixth ACM international conference on Web search and data mining
LaFT-tree: perceiving the expansion trace of one's circle of friends in online social networks
Proceedings of the sixth ACM international conference on Web search and data mining
Will you have a good sleep tonight?: sleep quality prediction with mobile phone
Proceedings of the 7th International Conference on Body Area Networks
Recommendation in reciprocal and bipartite social networks: a case study of online dating
SBP'13 Proceedings of the 6th international conference on Social Computing, Behavioral-Cultural Modeling and Prediction
Simultaneously detecting fake reviews and review spammers using factor graph model
Proceedings of the 5th Annual ACM Web Science Conference
Learning latent friendship propagation networks with interest awareness for link prediction
Proceedings of the 36th international ACM SIGIR conference on Research and development in information retrieval
Unsupervised link prediction using aggregative statistics on heterogeneous 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)
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We study the extent to which the formation of a two-way relationship can be predicted in a dynamic social network. A two-way (called reciprocal) relationship, usually developed from a one-way (parasocial) relationship, represents a more trustful relationship between people. Understanding the formation of two-way relationships can provide us insights into the micro-level dynamics of the social network, such as what is the underlying community structure and how users influence each other. Employing Twitter as a source for our experimental data, we propose a learning framework to formulate the problem of reciprocal relationship prediction into a graphical model. The framework incorporates social theories into a machine learning model. We demonstrate that it is possible to accurately infer 90% of reciprocal relationships in a dynamic network. Our study provides strong evidence of the existence of the structural balance among reciprocal relationships. In addition, we have some interesting findings, e.g., the likelihood of two "elite" users creating a reciprocal relationships is nearly 8 times higher than the likelihood of two ordinary users. More importantly, our findings have potential implications such as how social structures can be inferred from individuals' behaviors.