M
Meng Jiang
Researcher at University of Notre Dame
Publications - 164
Citations - 4266
Meng Jiang is an academic researcher from University of Notre Dame. The author has contributed to research in topics: Computer science & Graph (abstract data type). The author has an hindex of 26, co-authored 133 publications receiving 2750 citations. Previous affiliations of Meng Jiang include Association for Computing Machinery & University of Illinois at Urbana–Champaign.
Papers
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Journal ArticleDOI
Automated Phrase Mining from Massive Text Corpora
TL;DR: This paper proposed a framework for automated phrase mining, $\mathsf{AutoPhrase}$, which supports any language as long as a general knowledge base (e.g., Wikipedia) in that language is available, while benefiting from, but not requiring, a POS tagger.
Proceedings ArticleDOI
Mining topic-level influence in heterogeneous networks
TL;DR: A generative graphical model is proposed which utilizes the heterogeneous link information and the textual content associated with each node in the network to mine topic-level direct influence and a topic- level influence propagation and aggregation algorithm is proposed to derive the indirect influence between nodes.
Proceedings ArticleDOI
Social contextual recommendation
TL;DR: This paper investigates social recommendation on the basis of psychology and sociology studies, which exhibit two important factors: individual preference and interpersonal influence, and proposes a novel probabilistic matrix factorization method to fuse them in latent spaces.
Posted Content
Data Augmentation for Graph Neural Networks
TL;DR: This work shows that neural edge predictors can effectively encode class-homophilic structure to promote intra- class edges and demote inter-class edges in given graph structure, and introduces the GAug graph data augmentation framework, which leverages these insights to improve performance in GNN-based node classification via edge prediction.
Proceedings ArticleDOI
CatchSync: catching synchronized behavior in large directed graphs
TL;DR: This work proposes a fast and effective method, CatchSync, which exploits two of the tell-tale signs left in graphs by fraudsters, and introduces novel measures to quantify both concepts ("synchronicity" and "normality") and proposes a parameter-free algorithm that works on the resulting synchronicities-normality plots.