Incremental User Identification Across Social Networks Based on User-Guider Similarity Index

Identifying accounts across different online social networks that belong to the same user has attracted extensive attentions.However,existing techniques rely on given user seeds and ignore the dynamic changes of online social networks,which fails to generate high quality identification results.In order to solve this problem,we propose an incremental user identification method based on user-guider similarity index(called CURIOUS),which efficiently identifies users and well captures the changes of user features over time.Specifically,we first construct a novel user-guider similarity index(called USI)to speed up the matching between users.Second we propose a two-phase user identification strategy consisting of USI-based bidirectional user matching and seed-based user matching,which is effective even for incomplete networks.Finally,we propose incremental maintenance for both USI and the identification results,which dynamically captures the instant states of social networks.We conduct experimental studies based on three real-world social networks.The experiments demonstrate the effectiveness and the efficiency of our proposed method in comparison with traditional methods.Compared with the traditional methods,our method improves precision,recall and rank score by an average of 0.19,0.16 and 0.09 respectively,and reduces the time cost by an average of 81%.

Meta-Path-Based Search and Mining in Heterogeneous Information Networks

Information networks that can be extracted from many domains are widely studied recently. Different functions for mining these networks are proposed and developed, such as ranking, community detection, and link prediction. Most existing network studies are on homogeneous networks, where nodes and links are assumed from one single type. In reality, however, heterogeneous information networks can better model the real-world systems, which are typically semi-structured and typed, following a network schema. In order to mine these heterogeneous information networks directly, we propose to explore the meta structure of the information network, i.e., the network schema. The concepts of meta-paths are proposed to systematically capture numerous semantic relationships across multiple types of objects, which are defined as a path over the graph of network schema. Meta-paths can provide guidance for search and mining of the network and help analyze and understand the semantic meaning of the objects and relations in the network. Under this framework, similarity search and other mining tasks such as relationship prediction and clustering can be addressed by systematic exploration of the network meta structure. Moreover, with user＇s guidance or feedback, we can select the best meta-path or their weighted combination for a specific mining task.