Get our free extension to see links to code for papers anywhere online!

Chrome logo Add to Chrome

Firefox logo Add to Firefox
Picture for Tengfei Ma

Tengfei Ma

Constructing Contrastive samples via Summarization for Text Classification with limited annotations


Apr 11, 2021
Yangkai Du, Tengfei Ma, Lingfei Wu, Fangli Xu, Xuhong Zhang, Shouling Ji


  Access Paper or Ask Questions

Persistence Homology for Link Prediction: An Interactive View


Feb 20, 2021
Zuoyu Yan, Tengfei Ma, Liangcai Gao, Zhi Tang, Chao Chen

Figure 1 for Persistence Homology for Link Prediction: An Interactive View
Figure 2 for Persistence Homology for Link Prediction: An Interactive View
Figure 3 for Persistence Homology for Link Prediction: An Interactive View
Figure 4 for Persistence Homology for Link Prediction: An Interactive View

  Access Paper or Ask Questions

Wasserstein diffusion on graphs with missing attributes


Feb 06, 2021
Zhixian Chen, Tengfei Ma, Yangqiu Song, Yang Wang

Figure 1 for Wasserstein diffusion on graphs with missing attributes
Figure 2 for Wasserstein diffusion on graphs with missing attributes
Figure 3 for Wasserstein diffusion on graphs with missing attributes
Figure 4 for Wasserstein diffusion on graphs with missing attributes

  Access Paper or Ask Questions

BiGCN: A Bi-directional Low-Pass Filtering Graph Neural Network


Jan 14, 2021
Zhixian Chen, Tengfei Ma, Zhihua Jin, Yangqiu Song, Yang Wang

Figure 1 for BiGCN: A Bi-directional Low-Pass Filtering Graph Neural Network
Figure 2 for BiGCN: A Bi-directional Low-Pass Filtering Graph Neural Network
Figure 3 for BiGCN: A Bi-directional Low-Pass Filtering Graph Neural Network
Figure 4 for BiGCN: A Bi-directional Low-Pass Filtering Graph Neural Network

  Access Paper or Ask Questions

GNNVis: A Visual Analytics Approach for Prediction Error Diagnosis of Graph Neural Networks


Dec 03, 2020
Zhihua Jin, Yong Wang, Qianwen Wang, Yao Ming, Tengfei Ma, Huamin Qu

Figure 1 for GNNVis: A Visual Analytics Approach for Prediction Error Diagnosis of Graph Neural Networks
Figure 2 for GNNVis: A Visual Analytics Approach for Prediction Error Diagnosis of Graph Neural Networks
Figure 3 for GNNVis: A Visual Analytics Approach for Prediction Error Diagnosis of Graph Neural Networks
Figure 4 for GNNVis: A Visual Analytics Approach for Prediction Error Diagnosis of Graph Neural Networks
* 14 pages 
  Access Paper or Ask Questions

Exploiting Heterogeneous Graph Neural Networks with Latent Worker/Task Correlation Information for Label Aggregation in Crowdsourcing


Oct 25, 2020
Hanlu Wu, Tengfei Ma, Lingfei Wu, Shouling Ji

Figure 1 for Exploiting Heterogeneous Graph Neural Networks with Latent Worker/Task Correlation Information for Label Aggregation in Crowdsourcing
Figure 2 for Exploiting Heterogeneous Graph Neural Networks with Latent Worker/Task Correlation Information for Label Aggregation in Crowdsourcing
Figure 3 for Exploiting Heterogeneous Graph Neural Networks with Latent Worker/Task Correlation Information for Label Aggregation in Crowdsourcing
Figure 4 for Exploiting Heterogeneous Graph Neural Networks with Latent Worker/Task Correlation Information for Label Aggregation in Crowdsourcing

  Access Paper or Ask Questions

Deep Graph Matching and Searching for Semantic Code Retrieval


Oct 24, 2020
Xiang Ling, Lingfei Wu, Saizhuo Wang, Gaoning Pan, Tengfei Ma, Fangli Xu, Alex X. Liu, Chunming Wu, Shouling Ji

Figure 1 for Deep Graph Matching and Searching for Semantic Code Retrieval
Figure 2 for Deep Graph Matching and Searching for Semantic Code Retrieval
Figure 3 for Deep Graph Matching and Searching for Semantic Code Retrieval
Figure 4 for Deep Graph Matching and Searching for Semantic Code Retrieval
* 21 pages 
  Access Paper or Ask Questions

Unsupervised Reference-Free Summary Quality Evaluation via Contrastive Learning


Oct 05, 2020
Hanlu Wu, Tengfei Ma, Lingfei Wu, Tariro Manyumwa, Shouling Ji

Figure 1 for Unsupervised Reference-Free Summary Quality Evaluation via Contrastive Learning
Figure 2 for Unsupervised Reference-Free Summary Quality Evaluation via Contrastive Learning
Figure 3 for Unsupervised Reference-Free Summary Quality Evaluation via Contrastive Learning
Figure 4 for Unsupervised Reference-Free Summary Quality Evaluation via Contrastive Learning
* Long Paper in EMNLP 2020 
  Access Paper or Ask Questions

Hierarchical Graph Matching Networks for Deep Graph Similarity Learning


Jul 08, 2020
Xiang Ling, Lingfei Wu, Saizhuo Wang, Tengfei Ma, Fangli Xu, Alex X. Liu, Chunming Wu, Shouling Ji

Figure 1 for Hierarchical Graph Matching Networks for Deep Graph Similarity Learning
Figure 2 for Hierarchical Graph Matching Networks for Deep Graph Similarity Learning
Figure 3 for Hierarchical Graph Matching Networks for Deep Graph Similarity Learning
Figure 4 for Hierarchical Graph Matching Networks for Deep Graph Similarity Learning
* 17 pages 
  Access Paper or Ask Questions

CHEER: Rich Model Helps Poor Model via Knowledge Infusion


May 21, 2020
Cao Xiao, Trong Nghia Hoang, Shenda Hong, Tengfei Ma, Jimeng Sun

Figure 1 for CHEER: Rich Model Helps Poor Model via Knowledge Infusion
Figure 2 for CHEER: Rich Model Helps Poor Model via Knowledge Infusion
Figure 3 for CHEER: Rich Model Helps Poor Model via Knowledge Infusion
Figure 4 for CHEER: Rich Model Helps Poor Model via Knowledge Infusion
* Published in TKDE 
  Access Paper or Ask Questions

Repurpose Open Data to Discover Therapeutics for COVID-19 using Deep Learning


May 21, 2020
Xiangxiang Zeng, Xiang Song, Tengfei Ma, Xiaoqin Pan, Yadi Zhou, Yuan Hou, Zheng Zhang, George Karypis, Feixiong Cheng


  Access Paper or Ask Questions

Unsupervised Learning of Graph Hierarchical Abstractions with Differentiable Coarsening and Optimal Transport


Dec 24, 2019
Tengfei Ma, Jie Chen

Figure 1 for Unsupervised Learning of Graph Hierarchical Abstractions with Differentiable Coarsening and Optimal Transport
Figure 2 for Unsupervised Learning of Graph Hierarchical Abstractions with Differentiable Coarsening and Optimal Transport
Figure 3 for Unsupervised Learning of Graph Hierarchical Abstractions with Differentiable Coarsening and Optimal Transport
Figure 4 for Unsupervised Learning of Graph Hierarchical Abstractions with Differentiable Coarsening and Optimal Transport
* Code is available at https://github.com/matenure/OTCoarsening 
  Access Paper or Ask Questions

GENN: Predicting Correlated Drug-drug Interactions with Graph Energy Neural Networks


Oct 08, 2019
Tengfei Ma, Junyuan Shang, Cao Xiao, Jimeng Sun

Figure 1 for GENN: Predicting Correlated Drug-drug Interactions with Graph Energy Neural Networks
Figure 2 for GENN: Predicting Correlated Drug-drug Interactions with Graph Energy Neural Networks
Figure 3 for GENN: Predicting Correlated Drug-drug Interactions with Graph Energy Neural Networks
Figure 4 for GENN: Predicting Correlated Drug-drug Interactions with Graph Energy Neural Networks

  Access Paper or Ask Questions

DeepDrawing: A Deep Learning Approach to Graph Drawing


Jul 27, 2019
Yong Wang, Zhihua Jin, Qianwen Wang, Weiwei Cui, Tengfei Ma, Huamin Qu

Figure 1 for DeepDrawing: A Deep Learning Approach to Graph Drawing
Figure 2 for DeepDrawing: A Deep Learning Approach to Graph Drawing
Figure 3 for DeepDrawing: A Deep Learning Approach to Graph Drawing
Figure 4 for DeepDrawing: A Deep Learning Approach to Graph Drawing
* 11 pages 
  Access Paper or Ask Questions

MINA: Multilevel Knowledge-Guided Attention for Modeling Electrocardiography Signals


Jun 06, 2019
Shenda Hong, Cao Xiao, Tengfei Ma, Hongyan Li, Jimeng Sun

Figure 1 for MINA: Multilevel Knowledge-Guided Attention for Modeling Electrocardiography Signals
Figure 2 for MINA: Multilevel Knowledge-Guided Attention for Modeling Electrocardiography Signals
Figure 3 for MINA: Multilevel Knowledge-Guided Attention for Modeling Electrocardiography Signals
Figure 4 for MINA: Multilevel Knowledge-Guided Attention for Modeling Electrocardiography Signals
* Accepted to IJCAI 2019 
  Access Paper or Ask Questions

Pre-training of Graph Augmented Transformers for Medication Recommendation


Jun 02, 2019
Junyuan Shang, Tengfei Ma, Cao Xiao, Jimeng Sun

Figure 1 for Pre-training of Graph Augmented Transformers for Medication Recommendation
Figure 2 for Pre-training of Graph Augmented Transformers for Medication Recommendation
Figure 3 for Pre-training of Graph Augmented Transformers for Medication Recommendation
Figure 4 for Pre-training of Graph Augmented Transformers for Medication Recommendation
* IJCAI2019 
  Access Paper or Ask Questions

IPC: A Benchmark Data Set for Learning with Graph-Structured Data


May 15, 2019
Patrick Ferber, Tengfei Ma, Siyu Huo, Jie Chen, Michael Katz

Figure 1 for IPC: A Benchmark Data Set for Learning with Graph-Structured Data
Figure 2 for IPC: A Benchmark Data Set for Learning with Graph-Structured Data
Figure 3 for IPC: A Benchmark Data Set for Learning with Graph-Structured Data
Figure 4 for IPC: A Benchmark Data Set for Learning with Graph-Structured Data
* ICML 2019 Workshop on Learning and Reasoning with Graph-Structured Data. The data set is accessible from https://github.com/IBM/IPC-graph-data 
  Access Paper or Ask Questions

EvolveGCN: Evolving Graph Convolutional Networks for Dynamic Graphs


Feb 26, 2019
Aldo Pareja, Giacomo Domeniconi, Jie Chen, Tengfei Ma, Toyotaro Suzumura, Hiroki Kanezashi, Tim Kaler, Charles E. Leisersen

Figure 1 for EvolveGCN: Evolving Graph Convolutional Networks for Dynamic Graphs
Figure 2 for EvolveGCN: Evolving Graph Convolutional Networks for Dynamic Graphs
Figure 3 for EvolveGCN: Evolving Graph Convolutional Networks for Dynamic Graphs
Figure 4 for EvolveGCN: Evolving Graph Convolutional Networks for Dynamic Graphs

  Access Paper or Ask Questions

Reaching Data Confidentiality and Model Accountability on the CalTrain


Dec 07, 2018
Zhongshu Gu, Hani Jamjoom, Dong Su, Heqing Huang, Jialong Zhang, Tengfei Ma, Dimitrios Pendarakis, Ian Molloy

Figure 1 for Reaching Data Confidentiality and Model Accountability on the CalTrain
Figure 2 for Reaching Data Confidentiality and Model Accountability on the CalTrain
Figure 3 for Reaching Data Confidentiality and Model Accountability on the CalTrain
Figure 4 for Reaching Data Confidentiality and Model Accountability on the CalTrain

  Access Paper or Ask Questions

Scalable Graph Learning for Anti-Money Laundering: A First Look


Nov 30, 2018
Mark Weber, Jie Chen, Toyotaro Suzumura, Aldo Pareja, Tengfei Ma, Hiroki Kanezashi, Tim Kaler, Charles E. Leiserson, Tao B. Schardl

Figure 1 for Scalable Graph Learning for Anti-Money Laundering: A First Look
* NeurIPS 2018 Workshop on Challenges and Opportunities for AI in Financial Services: the Impact of Fairness, Explainability, Accuracy, and Privacy, Montreal, Canada 
  Access Paper or Ask Questions

Adaptive Planner Scheduling with Graph Neural Networks


Nov 03, 2018
Tengfei Ma, Patrick Ferber, Siyu Huo, Jie Chen, Michael Katz

Figure 1 for Adaptive Planner Scheduling with Graph Neural Networks
Figure 2 for Adaptive Planner Scheduling with Graph Neural Networks
Figure 3 for Adaptive Planner Scheduling with Graph Neural Networks
Figure 4 for Adaptive Planner Scheduling with Graph Neural Networks
* Data set is released at https://github.com/IBM/IPC-graph-data 
  Access Paper or Ask Questions

Constrained Generation of Semantically Valid Graphs via Regularizing Variational Autoencoders


Sep 19, 2018
Tengfei Ma, Jie Chen, Cao Xiao

Figure 1 for Constrained Generation of Semantically Valid Graphs via Regularizing Variational Autoencoders
Figure 2 for Constrained Generation of Semantically Valid Graphs via Regularizing Variational Autoencoders
Figure 3 for Constrained Generation of Semantically Valid Graphs via Regularizing Variational Autoencoders
Figure 4 for Constrained Generation of Semantically Valid Graphs via Regularizing Variational Autoencoders
* NIPS 2018 
  Access Paper or Ask Questions

AWE: Asymmetric Word Embedding for Textual Entailment


Sep 12, 2018
Tengfei Ma, Chiamin Wu, Cao Xiao, Jimeng Sun

Figure 1 for AWE: Asymmetric Word Embedding for Textual Entailment
Figure 2 for AWE: Asymmetric Word Embedding for Textual Entailment
Figure 3 for AWE: Asymmetric Word Embedding for Textual Entailment
Figure 4 for AWE: Asymmetric Word Embedding for Textual Entailment

  Access Paper or Ask Questions

RDPD: Rich Data Helps Poor Data via Imitation


Sep 07, 2018
Shenda Hong, Cao Xiao, Tengfei Ma, Hongyan Li, Jimeng Sun

Figure 1 for RDPD: Rich Data Helps Poor Data via Imitation
Figure 2 for RDPD: Rich Data Helps Poor Data via Imitation
Figure 3 for RDPD: Rich Data Helps Poor Data via Imitation
Figure 4 for RDPD: Rich Data Helps Poor Data via Imitation

  Access Paper or Ask Questions

GAMENet: Graph Augmented MEmory Networks for Recommending Medication Combination


Sep 06, 2018
Junyuan Shang, Cao Xiao, Tengfei Ma, Hongyan Li, Jimeng Sun

Figure 1 for GAMENet: Graph Augmented MEmory Networks for Recommending Medication Combination
Figure 2 for GAMENet: Graph Augmented MEmory Networks for Recommending Medication Combination
Figure 3 for GAMENet: Graph Augmented MEmory Networks for Recommending Medication Combination
Figure 4 for GAMENet: Graph Augmented MEmory Networks for Recommending Medication Combination

  Access Paper or Ask Questions

Drug Similarity Integration Through Attentive Multi-view Graph Auto-Encoders


Apr 28, 2018
Tengfei Ma, Cao Xiao, Jiayu Zhou, Fei Wang

Figure 1 for Drug Similarity Integration Through Attentive Multi-view Graph Auto-Encoders
Figure 2 for Drug Similarity Integration Through Attentive Multi-view Graph Auto-Encoders
Figure 3 for Drug Similarity Integration Through Attentive Multi-view Graph Auto-Encoders
Figure 4 for Drug Similarity Integration Through Attentive Multi-view Graph Auto-Encoders
* IJCAI 2018 
  Access Paper or Ask Questions

FastGCN: Fast Learning with Graph Convolutional Networks via Importance Sampling


Jan 30, 2018
Jie Chen, Tengfei Ma, Cao Xiao

* ICLR 2018 
  Access Paper or Ask Questions