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Bin Wu
Researcher at Beijing University of Posts and Telecommunications
Publications - 305
Citations - 4929
Bin Wu is an academic researcher from Beijing University of Posts and Telecommunications. The author has contributed to research in topics: Complex network & Graph (abstract data type). The author has an hindex of 30, co-authored 279 publications receiving 3891 citations. Previous affiliations of Bin Wu include Nanjing Tech University & Dalian University of Technology.
Papers
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Proceedings ArticleDOI
Semantic Path based Personalized Recommendation on Weighted Heterogeneous Information Networks
TL;DR: This paper is the first to propose the weighted HIN and weighted meta path concepts to subtly depict the path semantics through distinguishing different link attribute values, and proposes a semantic path based personalized recommendation method SemRec to predict the rating scores of users on items.
Journal ArticleDOI
HeteSim: A General Framework for Relevance Measure in Heterogeneous Networks
TL;DR: Empirical studies show that HeteSim can effectively and efficiently evaluate the relatedness of heterogeneous objects, which is crucial to many data mining tasks.
Proceedings ArticleDOI
Community detection in large-scale social networks
TL;DR: The algorithm ComTector (Community DeTector) is presented which is more efficient for the community detection in large-scale social networks based on the nature of overlapping communities in the real world and a general naming method is proposed by combining the topological information with the entity attributes to define the discovered communities.
Posted Content
HeteSim: A General Framework for Relevance Measure in Heterogeneous Networks
TL;DR: Zhang et al. as mentioned in this paper studied the relevance search problem in heterogeneous networks, where the task is to measure the relatedness of heterogeneous objects (including objects with the same type or different types).
Journal ArticleDOI
Multi-objective community detection in complex networks
TL;DR: It is demonstrated that a combination of two negatively correlated objectives under the multi-objective framework usually leads to remarkably better performance compared with either of the orignal single objectives, including even many popular algorithms.