• Publications
  • Influence
Network Embedding as Matrix Factorization: Unifying DeepWalk, LINE, PTE, and node2vec
TLDR
In this work, we show that all of the aforementioned models with negative sampling can be unified into the matrix factorization framework with closed forms. Expand
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Social influence analysis in large-scale networks
TLDR
We propose Topical Affinity Propagation (TAP) to model the topic-level social influence on large networks. Expand
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RiMOM: A Dynamic Multistrategy Ontology Alignment Framework
TLDR
We propose a systematic approach to quantitatively estimate the similarity characteristics for each alignment task and propose a strategy selection method to automatically combine the matching strategies based on estimated factors. Expand
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COSNET: Connecting Heterogeneous Social Networks with Local and Global Consistency
TLDR
We propose COSNET (COnnecting heterogeneous Social NETworks with local and global consistency), a novel energy-based model, to address this problem. Expand
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User-level sentiment analysis incorporating social networks
TLDR
We show that information about social relationships can be used to improve user-level sentiment analysis. Expand
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Social Influence Locality for Modeling Retweeting Behaviors
TLDR
We study an interesting phenomenon of social influence locality in a large microblogging network, which suggests that users' behaviors are mainly influenced by close friends in their ego networks. Expand
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A Unified Probabilistic Framework for Name Disambiguation in Digital Library
TLDR
We formalize the problem in a unified probabilistic framework, which incorporates both attributes and relationships, and propose a dynamic approach for estimating the number of people K. Expand
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Mining structural hole spanners through information diffusion in social networks
TLDR
The theory of structural holes suggests that individuals would benefit from filling the "holes" (called as structural hole spanners) between people or groups that are otherwise disconnected. Expand
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Detecting Community Kernels in Large Social Networks
TLDR
We propose Greedy and We BA, two efficient algorithms for finding community kernels in large social networks. Expand
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Who will follow you back?: reciprocal relationship prediction
TLDR
We study the extent to which the formation of a two-way relationship can be predicted in a dynamic social network. Expand
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