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Wei Chen
Researcher at Microsoft
Publications - 226
Citations - 14625
Wei Chen is an academic researcher from Microsoft. The author has contributed to research in topics: Maximization & Greedy algorithm. The author has an hindex of 47, co-authored 226 publications receiving 12843 citations. Previous affiliations of Wei Chen include University of British Columbia & Stony Brook University.
Papers
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Proceedings ArticleDOI
Efficient influence maximization in social networks
Wei Chen,Yajun Wang,Siyu Yang +2 more
TL;DR: Based on the results, it is believed that fine-tuned heuristics may provide truly scalable solutions to the influence maximization problem with satisfying influence spread and blazingly fast running time.
Proceedings ArticleDOI
Scalable influence maximization for prevalent viral marketing in large-scale social networks
Wei Chen,Chi Wang,Yajun Wang +2 more
TL;DR: The results from extensive simulations demonstrate that the proposed algorithm is currently the best scalable solution to the influence maximization problem and significantly outperforms all other scalable heuristics to as much as 100%--260% increase in influence spread.
Proceedings ArticleDOI
Scalable Influence Maximization in Social Networks under the Linear Threshold Model
Wei Chen,Yifei Yuan,Li Zhang +2 more
TL;DR: This paper proposes the first scalable influence maximization algorithm tailored for the linear threshold model, which is scalable to networks with millions of nodes and edges, is orders of magnitude faster than the greedy approximation algorithm proposed by Kempe et al. and its optimized versions, and performs consistently among the best algorithms.
Proceedings ArticleDOI
Prominent Features of Rumor Propagation in Online Social Media
TL;DR: A new periodic time series model that considers daily and external shock cycles, where the model demonstrates that rumor likely have fluctuations over time, and key structural and linguistic differences in the spread of rumors and non-rumors are identified.
Proceedings Article
Influence Blocking Maximization in Social Networks under the Competitive Linear Threshold Model.
TL;DR: An efficient algorithm CLDAG is designed, which utilizes the properties of the CLT model, and is able to provide best accuracy in par with the greedy algorithm and often better than other algorithms, while it is two orders of magnitude faster than the greedy algorithms.