Search Results for author: Zheng Gao

Found 9 papers, 3 papers with code

Efficient Personalized Community Detection via Genetic Evolution

no code implementations6 Sep 2020 Zheng Gao, Chun Guo, Xiaozhong Liu

Personalized community detection aims to generate communities associated with user need on graphs, which benefits many downstream tasks such as node recommendation and link prediction for users, etc.

Community Detection Link Prediction

Detecting User Community in Sparse Domain via Cross-Graph Pairwise Learning

no code implementations6 Sep 2020 Zheng Gao, Hongsong Li, Zhuoren Jiang, Xiaozhong Liu

In this paper, our model, Pairwise Cross-graph Community Detection (PCCD), is proposed to cope with the sparse graph problem by involving external graph knowledge to learn user pairwise community closeness instead of detecting direct communities.

Community Detection

Typilus: Neural Type Hints

1 code implementation6 Apr 2020 Miltiadis Allamanis, Earl T. Barr, Soline Ducousso, Zheng Gao

The network uses deep similarity learning to learn a TypeSpace -- a continuous relaxation of the discrete space of types -- and how to embed the type properties of a symbol (i. e. identifier) into it.

One-Shot Learning

AMAD: Adversarial Multiscale Anomaly Detection on High-Dimensional and Time-Evolving Categorical Data

no code implementations12 Jul 2019 Zheng Gao, Lin Guo, Chi Ma, Xiao Ma, Kai Sun, Hang Xiang, Xiaoqiang Zhu, Hongsong Li, Xiaozhong Liu

Anomaly detection is facing with emerging challenges in many important industry domains, such as cyber security and online recommendation and advertising.

Anomaly Detection

Neural Related Work Summarization with a Joint Context-driven Attention Mechanism

1 code implementation EMNLP 2018 Yongzhen Wang, Xiaozhong Liu, Zheng Gao

Conventional solutions to automatic related work summarization rely heavily on human-engineered features.

edge2vec: Representation learning using edge semantics for biomedical knowledge discovery

1 code implementation7 Sep 2018 Zheng Gao, Gang Fu, Chunping Ouyang, Satoshi Tsutsui, Xiaozhong Liu, Jeremy Yang, Christopher Gessner, Brian Foote, David Wild, Qi Yu, Ying Ding

We propose this method for its added value relative to existing graph analytical methodology, and in the real world context of biomedical knowledge discovery applicability.

Biomedical Information Retrieval Information Retrieval +2

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