Algorithms for clustering data
Algorithms for clustering data
Normalized Cuts and Image Segmentation
IEEE Transactions on Pattern Analysis and Machine Intelligence
Item-based collaborative filtering recommendation algorithms
Proceedings of the 10th international conference on World Wide Web
Kernel k-means: spectral clustering and normalized cuts
Proceedings of the tenth ACM SIGKDD international conference on Knowledge discovery and data mining
Proceedings of the eleventh ACM SIGKDD international conference on Knowledge discovery in data mining
Analysis of topological characteristics of huge online social networking services
Proceedings of the 16th international conference on World Wide Web
Measurement and analysis of online social networks
Proceedings of the 7th ACM SIGCOMM conference on Internet measurement
Proceedings of the forty-first annual ACM symposium on Theory of computing
Power-Law Distributions in Empirical Data
SIAM Review
Networks, Crowds, and Markets: Reasoning About a Highly Connected World
Networks, Crowds, and Markets: Reasoning About a Highly Connected World
New perspectives and methods in link prediction
Proceedings of the 16th ACM SIGKDD international conference on Knowledge discovery and data mining
Exploiting place features in link prediction on location-based social networks
Proceedings of the 17th ACM SIGKDD international conference on Knowledge discovery and data mining
Friendship and mobility: user movement in location-based social networks
Proceedings of the 17th ACM SIGKDD international conference on Knowledge discovery and data mining
IEEE Transactions on Pattern Analysis and Machine Intelligence
Exploring personal impact for group recommendation
Proceedings of the 21st ACM international conference on Information and knowledge management
On how event size and interactivity affect social networks
CHI '13 Extended Abstracts on Human Factors in Computing Systems
LCARS: a location-content-aware recommender system
Proceedings of the 19th ACM SIGKDD international conference on Knowledge discovery and data mining
Combining latent factor model with location features for event-based group recommendation
Proceedings of the 19th ACM SIGKDD international conference on Knowledge discovery and data mining
Proceedings of the 22nd ACM international conference on Conference on information & knowledge management
Hybrid event recommendation using linked data and user diversity
Proceedings of the 7th ACM conference on Recommender systems
Mining groups of common interest: discovering topical communities with network flows
MLDM'13 Proceedings of the 9th international conference on Machine Learning and Data Mining in Pattern Recognition
An introduction to the special issue on cross-community mining
Personal and Ubiquitous Computing
Cross-domain community detection in heterogeneous social networks
Personal and Ubiquitous Computing
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Newly emerged event-based online social services, such as Meetup and Plancast, have experienced increased popularity and rapid growth. From these services, we observed a new type of social network - event-based social network (EBSN). An EBSN does not only contain online social interactions as in other conventional online social networks, but also includes valuable offline social interactions captured in offline activities. By analyzing real data collected from Meetup, we investigated EBSN properties and discovered many unique and interesting characteristics, such as heavy-tailed degree distributions and strong locality of social interactions. We subsequently studied the heterogeneous nature (co-existence of both online and offline social interactions) of EBSNs on two challenging problems: community detection and information flow. We found that communities detected in EBSNs are more cohesive than those in other types of social networks (e.g. location-based social networks). In the context of information flow, we studied the event recommendation problem. By experimenting various information diffusion patterns, we found that a community-based diffusion model that takes into account of both online and offline interactions provides the best prediction power. This paper is the first research to study EBSNs at scale and paves the way for future studies on this new type of social network. A sample dataset of this study can be downloaded from http://www.largenetwork.org/ebsn.