Diffusion Kernels on Graphs and Other Discrete Input Spaces
ICML '02 Proceedings of the Nineteenth International Conference on Machine Learning
The link prediction problem for social networks
CIKM '03 Proceedings of the twelfth international conference on Information and knowledge management
Measurement and analysis of online social networks
Proceedings of the 7th ACM SIGCOMM conference on Internet measurement
Predicting tie strength with social media
Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
The slashdot zoo: mining a social network with negative edges
Proceedings of the 18th international conference on World wide web
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
Modeling relationship strength in online social networks
Proceedings of the 19th international conference on World wide web
LINKREC: a unified framework for link recommendation with user attributes and graph structure
Proceedings of the 19th international conference on World wide web
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
Recommending twitter users to follow using content and collaborative filtering approaches
Proceedings of the fourth ACM conference on Recommender systems
Temporal Link Prediction Using Matrix and Tensor Factorizations
ACM Transactions on Knowledge Discovery from Data (TKDD)
Strength of social influence in trust networks in product review sites
Proceedings of the fourth ACM international conference on Web search 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
Improving Recommender Systems by Incorporating Social Contextual Information
ACM Transactions on Information Systems (TOIS)
Link prediction via matrix factorization
ECML PKDD'11 Proceedings of the 2011 European conference on Machine learning and knowledge discovery in databases - Volume Part II
Product recommendation and rating prediction based on multi-modal social networks
Proceedings of the fifth ACM conference on Recommender systems
Social link recommendation by learning hidden topics
Proceedings of the fifth ACM conference on Recommender systems
Link prediction: the power of maximal entropy random walk
Proceedings of the 20th ACM international conference on Information and knowledge management
Exploiting longer cycles for link prediction in signed networks
Proceedings of the 20th ACM international conference on Information and knowledge management
Modeling social strength in social media community via kernel-based learning
MM '11 Proceedings of the 19th ACM international conference on Multimedia
Inferring social ties across heterogenous networks
Proceedings of the fifth ACM international conference on Web search and data mining
Social network-based recommendation: a graph random walk kernel approach
Proceedings of the 12th ACM/IEEE-CS joint conference on Digital Libraries
Friend or frenemy?: predicting signed ties in social networks
SIGIR '12 Proceedings of the 35th international ACM SIGIR conference on Research and development in information retrieval
Mining competitive relationships by learning across heterogeneous networks
Proceedings of the 21st ACM international conference on Information and knowledge management
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
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
LAFT-Explorer: inferring, visualizing and predicting how your social network expands
Proceedings of the 19th ACM SIGKDD international conference on Knowledge discovery and data mining
What is the added value of negative links in online social networks?
Proceedings of the 22nd international conference on World Wide Web
Mining structural hole spanners through information diffusion in social networks
Proceedings of the 22nd international conference on World Wide Web
Predicting positive and negative links in signed social networks by transfer learning
Proceedings of the 22nd international conference on World Wide Web
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Together with the sign (positive or negative) and strength (strong or weak), the directionality is also an important property of social ties, though usually ignored in undirected social networks for its invisibility. However, we believe most social ties are natively directed, and the awareness of directionality can improve our understanding about the network structures and further benefit social network analysis and mining tasks. Thus it's appealing to study whether there exist interesting patterns about directionality in social networks and whether we can learn the directions for undirected networks based on these patterns. In this study, we engage in the investigation of directionality patterns on real-world directed social networks and summarize our findings using four consistency hypotheses. Based on these hypotheses, we propose ReDirect, an optimization framework which makes it possible to infer the hidden directions of undirected social ties based on the network topology only. This general framework can incorporate various predictive models under specific scenarios. Furthermore, we show how to improve ReDirect by introducing semi/self-supervision in the framework and how to construct the self-labeled training data using simple but effective heuristics. Experimental results show that even without external information, our approach can recover the directions of networks effectively. Moreover, we're quite surprising to find that ReDirect can benefit predictive tasks remarkably, with a case study of link prediction. In experiments the redirected networks inferred using ReDirect are proven much more informative than original undirected ones and can improve the prediction performance significantly. It convinces us that ReDirect can be a beneficial general data preprocess tool for various network analysis and mining tasks by uncovering the hidden directions of undirected social networks.