TAILIEUCHUNG - Event-based Social Networks: Linking the Online and Offline Social Worlds

We measure the reliable transport of event features from source nodes to the sink in terms of the number of received data packets. Regardless of any application-specific metric that may actually be used, the number of received data packets is closely related to the amount of information acquired by the sink for the detection and extraction of event features. Hence, this serves as a simple but adequate event reliability measure at the transport level. The observed and desired event reliabilities are now defined as follows: Definition 1: The observed event reliability, , is the number of received data packets in decision interval at the sink. Definition 2: The desired event reliability,. | Event-based Social Networks Linking the Online and Offline Social Worlds Xingjie Liu Qi Hey Yuanyuan Tiany Wang-Chien Lee John McPherson1 Jiawei Han The Pennsylvania State University y IBM Almaden Research Center University of Illinois at Urbana-Champaign xzl106 wlee @ y heq ytian jmcphers @ hanj@ ABSTRACT 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 coexistence 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 . 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 ebsn. Categories and Subject Descriptors Information Storage and Retrieval Systems and Software - Information networks General Terms Algorithms Experimentation. Keywords Event based .

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