Suhang Wang

Professor at Pennsylvania State University

About

Dr. Suhang Wang is an Assistant Professor of Computer Science and Engineering at Pennsylvania State University. He received his PhD in Computer Science from Arizona State University in 2018, and his Master's degree in Electrical Engineering: Systems from the University of Michigan in 2013. Before joining Penn State, Dr. Wang was a postdoctoral researcher at the University of California, Santa Barbara. His research interests include natural language processing, artificial intelligence, and machine learning. He was recognized for his work at the International Conference on Knowledge Discovery and Data Mining in 2017 and the Fifth ACM International Conference on Web Search and Data Mining in 2016.

Education

Arizona State University

PhD, Computer Science / July, 2018

Tempe, Arizona, United States of America

University of Michigan

MS, Electrical Engineering: Systems / December, 2013

Ann Arbor, Michigan, United States of America

Shanghai Jiao Tong University

BS, Electrical and Computer Engineering / July, 2012

Shanghai

University of Michigan

BS, Electrical Engineering / April, 2012

Ann Arbor, Michigan, United States of America

Experience

Pennsylvania State University

Assistant Professor / August, 2018Present

Publications

Fake News Detection on Social Media

ACM SIGKDD Explorations Newsletter / Sep 01, 2017

Shu, K., Sliva, A., Wang, S., Tang, J., & Liu, H. (2017). Fake News Detection on Social Media. ACM SIGKDD Explorations Newsletter, 19(1), 22–36. https://doi.org/10.1145/3137597.3137600

Feature Selection

ACM Computing Surveys / Dec 06, 2017

Li, J., Cheng, K., Wang, S., Morstatter, F., Trevino, R. P., Tang, J., & Liu, H. (2017). Feature Selection. ACM Computing Surveys, 50(6), 1–45. https://doi.org/10.1145/3136625

FakeNewsNet: A Data Repository with News Content, Social Context, and Spatiotemporal Information for Studying Fake News on Social Media

Big Data / Jun 01, 2020

Shu, K., Mahudeswaran, D., Wang, S., Lee, D., & Liu, H. (2020). FakeNewsNet: A Data Repository with News Content, Social Context, and Spatiotemporal Information for Studying Fake News on Social Media. Big Data, 8(3), 171–188. https://doi.org/10.1089/big.2020.0062

Beyond News Contents

Proceedings of the Twelfth ACM International Conference on Web Search and Data Mining / Jan 30, 2019

Shu, K., Wang, S., & Liu, H. (2019). Beyond News Contents. Proceedings of the Twelfth ACM International Conference on Web Search and Data Mining. https://doi.org/10.1145/3289600.3290994

dEFEND

Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining / Jul 25, 2019

Shu, K., Cui, L., Wang, S., Lee, D., & Liu, H. (2019). dEFEND. Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining. https://doi.org/10.1145/3292500.3330935

Understanding User Profiles on Social Media for Fake News Detection

2018 IEEE Conference on Multimedia Information Processing and Retrieval (MIPR) / Apr 01, 2018

Shu, K., Wang, S., & Liu, H. (2018). Understanding User Profiles on Social Media for Fake News Detection. 2018 IEEE Conference on Multimedia Information Processing and Retrieval (MIPR). https://doi.org/10.1109/mipr.2018.00092

Graph Structure Learning for Robust Graph Neural Networks

Proceedings of the 26th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining / Aug 20, 2020

Jin, W., Ma, Y., Liu, X., Tang, X., Wang, S., & Tang, J. (2020). Graph Structure Learning for Robust Graph Neural Networks. Proceedings of the 26th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining. https://doi.org/10.1145/3394486.3403049

Unsupervised Fake News Detection on Social Media: A Generative Approach

Proceedings of the AAAI Conference on Artificial Intelligence / Jul 17, 2019

Yang, S., Shu, K., Wang, S., Gu, R., Wu, F., & Liu, H. (2019). Unsupervised Fake News Detection on Social Media: A Generative Approach. Proceedings of the AAAI Conference on Artificial Intelligence, 33(01), 5644–5651. https://doi.org/10.1609/aaai.v33i01.33015644

What Your Images Reveal

Proceedings of the 26th International Conference on World Wide Web / Apr 03, 2017

Wang, S., Wang, Y., Tang, J., Shu, K., Ranganath, S., & Liu, H. (2017). What Your Images Reveal. Proceedings of the 26th International Conference on World Wide Web. https://doi.org/10.1145/3038912.3052638

Signed Network Embedding in Social Media

Proceedings of the 2017 SIAM International Conference on Data Mining / Jun 09, 2017

Wang, S., Tang, J., Aggarwal, C., Chang, Y., & Liu, H. (2017). Signed Network Embedding in Social Media. Proceedings of the 2017 SIAM International Conference on Data Mining, 327–335. https://doi.org/10.1137/1.9781611974973.37

User Identity Linkage across Online Social Networks

ACM SIGKDD Explorations Newsletter / Mar 22, 2017

Shu, K., Wang, S., Tang, J., Zafarani, R., & Liu, H. (2017). User Identity Linkage across Online Social Networks. ACM SIGKDD Explorations Newsletter, 18(2), 5–17. https://doi.org/10.1145/3068777.3068781

Embedded Unsupervised Feature Selection

Proceedings of the AAAI Conference on Artificial Intelligence / Feb 10, 2015

Wang, S., Tang, J., & Liu, H. (2015). Embedded Unsupervised Feature Selection. Proceedings of the AAAI Conference on Artificial Intelligence, 29(1). https://doi.org/10.1609/aaai.v29i1.9211

Preprint repository arXiv achieves milestone million uploads

Physics Today / Jan 01, 2014

Preprint repository arXiv achieves milestone million uploads. (2014). Physics Today. https://doi.org/10.1063/pt.5.028530

The role of user profiles for fake news detection

Proceedings of the 2019 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining / Aug 27, 2019

Shu, K., Zhou, X., Wang, S., Zafarani, R., & Liu, H. (2019). The role of user profiles for fake news detection. Proceedings of the 2019 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining. https://doi.org/10.1145/3341161.3342927

Sentiment Analysis for Social Media Images

2015 IEEE International Conference on Data Mining Workshop (ICDMW) / Nov 01, 2015

Wang, Y., & Li, B. (2015). Sentiment Analysis for Social Media Images. 2015 IEEE International Conference on Data Mining Workshop (ICDMW). https://doi.org/10.1109/icdmw.2015.142

Learning Word Representations for Sentiment Analysis

Cognitive Computation / Aug 17, 2017

Li, Y., Pan, Q., Yang, T., Wang, S., Tang, J., & Cambria, E. (2017). Learning Word Representations for Sentiment Analysis. Cognitive Computation, 9(6), 843–851. https://doi.org/10.1007/s12559-017-9492-2

Hierarchical Propagation Networks for Fake News Detection: Investigation and Exploitation

Proceedings of the International AAAI Conference on Web and Social Media / May 26, 2020

Shu, K., Mahudeswaran, D., Wang, S., & Liu, H. (2020). Hierarchical Propagation Networks for Fake News Detection: Investigation and Exploitation. Proceedings of the International AAAI Conference on Web and Social Media, 14, 626–637. https://doi.org/10.1609/icwsm.v14i1.7329

Adversarial Attacks on Graph Neural Networks via Node Injections: A Hierarchical Reinforcement Learning Approach

Proceedings of The Web Conference 2020 / Apr 20, 2020

Sun, Y., Wang, S., Tang, X., Hsieh, T.-Y., & Honavar, V. (2020). Adversarial Attacks on Graph Neural Networks via Node Injections: A Hierarchical Reinforcement Learning Approach. Proceedings of The Web Conference 2020. https://doi.org/10.1145/3366423.3380149

A Generative Model for category text generation

Information Sciences / Jun 01, 2018

Li, Y., Pan, Q., Wang, S., Yang, T., & Cambria, E. (2018). A Generative Model for category text generation. Information Sciences, 450, 301–315. https://doi.org/10.1016/j.ins.2018.03.050

GraphSMOTE: Imbalanced Node Classification on Graphs with Graph Neural Networks

Proceedings of the 14th ACM International Conference on Web Search and Data Mining / Mar 08, 2021

Zhao, T., Zhang, X., & Wang, S. (2021). GraphSMOTE: Imbalanced Node Classification on Graphs with Graph Neural Networks. Proceedings of the 14th ACM International Conference on Web Search and Data Mining. https://doi.org/10.1145/3437963.3441720

About the social role of child and adolescent psychiatrists in times of epidemic

IACAPAP ArXiv / Jan 01, 2020

Falissard, B. (2020). About the social role of child and adolescent psychiatrists in times of epidemic. IACAPAP ArXiv. https://doi.org/10.14744/iacapaparxiv.2020.20004

Transferring Robustness for Graph Neural Network Against Poisoning Attacks

Proceedings of the 13th International Conference on Web Search and Data Mining / Jan 20, 2020

Tang, X., Li, Y., Sun, Y., Yao, H., Mitra, P., & Wang, S. (2020). Transferring Robustness for Graph Neural Network Against Poisoning Attacks. Proceedings of the 13th International Conference on Web Search and Data Mining. https://doi.org/10.1145/3336191.3371851

Say No to the Discrimination: Learning Fair Graph Neural Networks with Limited Sensitive Attribute Information

Proceedings of the 14th ACM International Conference on Web Search and Data Mining / Mar 08, 2021

Dai, E., & Wang, S. (2021). Say No to the Discrimination: Learning Fair Graph Neural Networks with Limited Sensitive Attribute Information. Proceedings of the 14th ACM International Conference on Web Search and Data Mining. https://doi.org/10.1145/3437963.3441752

Discriminative graph regularized extreme learning machine and its application to face recognition

Neurocomputing / Feb 01, 2015

Peng, Y., Wang, S., Long, X., & Lu, B.-L. (2015). Discriminative graph regularized extreme learning machine and its application to face recognition. Neurocomputing, 149, 340–353. https://doi.org/10.1016/j.neucom.2013.12.065

Linked Document Embedding for Classification

Proceedings of the 25th ACM International on Conference on Information and Knowledge Management / Oct 24, 2016

Wang, S., Tang, J., Aggarwal, C., & Liu, H. (2016). Linked Document Embedding for Classification. Proceedings of the 25th ACM International on Conference on Information and Knowledge Management. https://doi.org/10.1145/2983323.2983755

Recommendation with Social Dimensions

Proceedings of the AAAI Conference on Artificial Intelligence / Feb 21, 2016

Tang, J., Wang, S., Hu, X., Yin, D., Bi, Y., Chang, Y., & Liu, H. (2016). Recommendation with Social Dimensions. Proceedings of the AAAI Conference on Artificial Intelligence, 30(1). https://doi.org/10.1609/aaai.v30i1.9976

Graph Few-Shot Learning via Knowledge Transfer

Proceedings of the AAAI Conference on Artificial Intelligence / Apr 03, 2020

Yao, H., Zhang, C., Wei, Y., Jiang, M., Wang, S., Huang, J., Chawla, N., & Li, Z. (2020). Graph Few-Shot Learning via Knowledge Transfer. Proceedings of the AAAI Conference on Artificial Intelligence, 34(04), 6656–6663. https://doi.org/10.1609/aaai.v34i04.6142

SAME

Proceedings of the 2019 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining / Aug 27, 2019

Cui, L., Wang, S., & Lee, D. (2019). SAME. Proceedings of the 2019 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining. https://doi.org/10.1145/3341161.3342894

Attributed Signed Network Embedding

Proceedings of the 2017 ACM on Conference on Information and Knowledge Management / Nov 06, 2017

Wang, S., Aggarwal, C., Tang, J., & Liu, H. (2017). Attributed Signed Network Embedding. Proceedings of the 2017 ACM on Conference on Information and Knowledge Management. https://doi.org/10.1145/3132847.3132905

DETERRENT: Knowledge Guided Graph Attention Network for Detecting Healthcare Misinformation

Proceedings of the 26th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining / Aug 20, 2020

Cui, L., Seo, H., Tabar, M., Ma, F., Wang, S., & Lee, D. (2020). DETERRENT: Knowledge Guided Graph Attention Network for Detecting Healthcare Misinformation. Proceedings of the 26th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining. https://doi.org/10.1145/3394486.3403092

Joint Modeling of Local and Global Temporal Dynamics for Multivariate Time Series Forecasting with Missing Values

Proceedings of the AAAI Conference on Artificial Intelligence / Apr 03, 2020

Tang, X., Yao, H., Sun, Y., Aggarwal, C., Mitra, P., & Wang, S. (2020). Joint Modeling of Local and Global Temporal Dynamics for Multivariate Time Series Forecasting with Missing Values. Proceedings of the AAAI Conference on Artificial Intelligence, 34(04), 5956–5963. https://doi.org/10.1609/aaai.v34i04.6056

ArXiv preprint server plans multimillion-dollar overhaul

Nature / Jun 29, 2016

Van Noorden, R. (2016). ArXiv preprint server plans multimillion-dollar overhaul. Nature, 534(7609), 602–602. https://doi.org/10.1038/534602a

Mining Disinformation and Fake News: Concepts, Methods, and Recent Advancements

Lecture Notes in Social Networks / Jan 01, 2020

Shu, K., Wang, S., Lee, D., & Liu, H. (2020). Mining Disinformation and Fake News: Concepts, Methods, and Recent Advancements. Disinformation, Misinformation, and Fake News in Social Media, 1–19. https://doi.org/10.1007/978-3-030-42699-6_1

Ginger Cannot Cure Cancer: Battling Fake Health News with a Comprehensive Data Repository

Proceedings of the International AAAI Conference on Web and Social Media / May 26, 2020

Dai, E., Sun, Y., & Wang, S. (2020). Ginger Cannot Cure Cancer: Battling Fake Health News with a Comprehensive Data Repository. Proceedings of the International AAAI Conference on Web and Social Media, 14, 853–862. https://doi.org/10.1609/icwsm.v14i1.7350

Learning binary codes with neural collaborative filtering for efficient recommendation systems

Knowledge-Based Systems / May 01, 2019

Li, Y., Wang, S., Pan, Q., Peng, H., Yang, T., & Cambria, E. (2019). Learning binary codes with neural collaborative filtering for efficient recommendation systems. Knowledge-Based Systems, 172, 64–75. https://doi.org/10.1016/j.knosys.2019.02.012

Personalized Privacy-Preserving Social Recommendation

Proceedings of the AAAI Conference on Artificial Intelligence / Apr 29, 2018

Meng, X., Wang, S., Shu, K., Li, J., Chen, B., Liu, H., & Zhang, Y. (2018). Personalized Privacy-Preserving Social Recommendation. Proceedings of the AAAI Conference on Artificial Intelligence, 32(1). https://doi.org/10.1609/aaai.v32i1.11714

Toward Dual Roles of Users in Recommender Systems

Proceedings of the 24th ACM International on Conference on Information and Knowledge Management / Oct 17, 2015

Wang, S., Tang, J., & Liu, H. (2015). Toward Dual Roles of Users in Recommender Systems. Proceedings of the 24th ACM International on Conference on Information and Knowledge Management. https://doi.org/10.1145/2806416.2806520

Disentangled Variational Auto-Encoder for semi-supervised learning

Information Sciences / May 01, 2019

Li, Y., Pan, Q., Wang, S., Peng, H., Yang, T., & Cambria, E. (2019). Disentangled Variational Auto-Encoder for semi-supervised learning. Information Sciences, 482, 73–85. https://doi.org/10.1016/j.ins.2018.12.057

Graph Adversarial Attack via Rewiring

Proceedings of the 27th ACM SIGKDD Conference on Knowledge Discovery & Data Mining / Aug 14, 2021

Ma, Y., Wang, S., Derr, T., Wu, L., & Tang, J. (2021). Graph Adversarial Attack via Rewiring. Proceedings of the 27th ACM SIGKDD Conference on Knowledge Discovery & Data Mining. https://doi.org/10.1145/3447548.3467416

Exploring Hierarchical Structures for Recommender Systems

IEEE Transactions on Knowledge and Data Engineering / Jun 01, 2018

Wang, S., Tang, J., Wang, Y., & Liu, H. (2018). Exploring Hierarchical Structures for Recommender Systems. IEEE Transactions on Knowledge and Data Engineering, 30(6), 1022–1035. https://doi.org/10.1109/tkde.2018.2789443

Deep Headline Generation for Clickbait Detection

2018 IEEE International Conference on Data Mining (ICDM) / Nov 01, 2018

Shu, K., Wang, S., Le, T., Lee, D., & Liu, H. (2018). Deep Headline Generation for Clickbait Detection. 2018 IEEE International Conference on Data Mining (ICDM). https://doi.org/10.1109/icdm.2018.00062

Exploiting Emotional Information for Trust/Distrust Prediction

Proceedings of the 2016 SIAM International Conference on Data Mining / Jun 30, 2016

Beigi, G., Tang, J., Wang, S., & Liu, H. (2016). Exploiting Emotional Information for Trust/Distrust Prediction. Proceedings of the 2016 SIAM International Conference on Data Mining. https://doi.org/10.1137/1.9781611974348.10

MALCOM: Generating Malicious Comments to Attack Neural Fake News Detection Models

2020 IEEE International Conference on Data Mining (ICDM) / Nov 01, 2020

Le, T., Wang, S., & Lee, D. (2020). MALCOM: Generating Malicious Comments to Attack Neural Fake News Detection Models. 2020 IEEE International Conference on Data Mining (ICDM). https://doi.org/10.1109/icdm50108.2020.00037

Enhanced low-rank representation via sparse manifold adaption for semi-supervised learning

Neural Networks / May 01, 2015

Peng, Y., Lu, B.-L., & Wang, S. (2015). Enhanced low-rank representation via sparse manifold adaption for semi-supervised learning. Neural Networks, 65, 1–17. https://doi.org/10.1016/j.neunet.2015.01.001

MEGAN: A Generative Adversarial Network for Multi-View Network Embedding

Proceedings of the Twenty-Eighth International Joint Conference on Artificial Intelligence / Aug 01, 2019

Sun, Y., Wang, S., Hsieh, T.-Y., Tang, X., & Honavar, V. (2019). MEGAN: A Generative Adversarial Network for Multi-View Network Embedding. Proceedings of the Twenty-Eighth International Joint Conference on Artificial Intelligence. https://doi.org/10.24963/ijcai.2019/489

Using a Random Forest to Inspire a Neural Network and Improving on It

Proceedings of the 2017 SIAM International Conference on Data Mining / Jun 09, 2017

Wang, S., Aggarwal, C., & Liu, H. (2017). Using a Random Forest to Inspire a Neural Network and Improving on It. Proceedings of the 2017 SIAM International Conference on Data Mining, 1–9. https://doi.org/10.1137/1.9781611974973.1

Feature Selection

Encyclopedia of Machine Learning and Data Mining / Jan 01, 2016

Wang, S., Tang, J., & Liu, H. (2016). Feature Selection. Encyclopedia of Machine Learning and Data Mining, 1–9. https://doi.org/10.1007/978-1-4899-7502-7_101-1

GRACE: Generating Concise and Informative Contrastive Sample to Explain Neural Network Model's Prediction

Proceedings of the 26th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining / Aug 20, 2020

Le, T., Wang, S., & Lee, D. (2020). GRACE: Generating Concise and Informative Contrastive Sample to Explain Neural Network Model’s Prediction. Proceedings of the 26th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining. https://doi.org/10.1145/3394486.3403066

Explainable Multivariate Time Series Classification

Proceedings of the 14th ACM International Conference on Web Search and Data Mining / Mar 08, 2021

Hsieh, T.-Y., Wang, S., Sun, Y., & Honavar, V. (2021). Explainable Multivariate Time Series Classification. Proceedings of the 14th ACM International Conference on Web Search and Data Mining. https://doi.org/10.1145/3437963.3441815

CrossFire

Proceedings of the Eleventh ACM International Conference on Web Search and Data Mining / Feb 02, 2018

Shu, K., Wang, S., Tang, J., Wang, Y., & Liu, H. (2018). CrossFire. Proceedings of the Eleventh ACM International Conference on Web Search and Data Mining. https://doi.org/10.1145/3159652.3159692

PPP: Joint Pointwise and Pairwise Image Label Prediction

2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) / Jun 01, 2016

Wang, Y., Wang, S., Tang, J., Liu, H., & Li, B. (2016). PPP: Joint Pointwise and Pairwise Image Label Prediction. 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR). https://doi.org/10.1109/cvpr.2016.646

dEFEND

Proceedings of the 28th ACM International Conference on Information and Knowledge Management / Nov 03, 2019

Cui, L., Shu, K., Wang, S., Lee, D., & Liu, H. (2019). dEFEND. Proceedings of the 28th ACM International Conference on Information and Knowledge Management. https://doi.org/10.1145/3357384.3357862

NRGNN

Proceedings of the 27th ACM SIGKDD Conference on Knowledge Discovery & Data Mining / Aug 14, 2021

Dai, E., Aggarwal, C., & Wang, S. (2021). NRGNN. Proceedings of the 27th ACM SIGKDD Conference on Knowledge Discovery & Data Mining. https://doi.org/10.1145/3447548.3467364

CLARE: A Joint Approach to Label Classification and Tag Recommendation

Proceedings of the AAAI Conference on Artificial Intelligence / Feb 10, 2017

Wang, Y., Wang, S., Tang, J., Qi, G., Liu, H., & Li, B. (2017). CLARE: A Joint Approach to Label Classification and Tag Recommendation. Proceedings of the AAAI Conference on Artificial Intelligence, 31(1). https://doi.org/10.1609/aaai.v31i1.10479

Learning How to Propagate Messages in Graph Neural Networks

Proceedings of the 27th ACM SIGKDD Conference on Knowledge Discovery & Data Mining / Aug 14, 2021

Xiao, T., Chen, Z., Wang, D., & Wang, S. (2021). Learning How to Propagate Messages in Graph Neural Networks. Proceedings of the 27th ACM SIGKDD Conference on Knowledge Discovery & Data Mining. https://doi.org/10.1145/3447548.3467451

Unsupervised Representation Learning of Spatial Data via Multimodal Embedding

Proceedings of the 28th ACM International Conference on Information and Knowledge Management / Nov 03, 2019

Jenkins, P., Farag, A., Wang, S., & Li, Z. (2019). Unsupervised Representation Learning of Spatial Data via Multimodal Embedding. Proceedings of the 28th ACM International Conference on Information and Knowledge Management. https://doi.org/10.1145/3357384.3358001

Knowing your FATE

Proceedings of the 26th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining / Aug 20, 2020

Tang, X., Liu, Y., Shah, N., Shi, X., Mitra, P., & Wang, S. (2020). Knowing your FATE. Proceedings of the 26th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining. https://doi.org/10.1145/3394486.3403276

Towards Interpretation of Recommender Systems with Sorted Explanation Paths

2018 IEEE International Conference on Data Mining (ICDM) / Nov 01, 2018

Yang, F., Liu, N., Wang, S., & Hu, X. (2018). Towards Interpretation of Recommender Systems with Sorted Explanation Paths. 2018 IEEE International Conference on Data Mining (ICDM). https://doi.org/10.1109/icdm.2018.00082

Fairness, explainability, privacy, and robustness for trustworthy algorithmic decision-making

Big Data Analytics in Chemoinformatics and Bioinformatics / Jan 01, 2023

Majumdar, S. (2023). Fairness, explainability, privacy, and robustness for trustworthy algorithmic decision-making. Big Data Analytics in Chemoinformatics and Bioinformatics, 61–95. https://doi.org/10.1016/b978-0-323-85713-0.00017-7

Towards Self-Explainable Graph Neural Network

Proceedings of the 30th ACM International Conference on Information & Knowledge Management / Oct 26, 2021

Dai, E., & Wang, S. (2021). Towards Self-Explainable Graph Neural Network. Proceedings of the 30th ACM International Conference on Information & Knowledge Management. https://doi.org/10.1145/3459637.3482306

Privacy Preserving Text Representation Learning

Proceedings of the 30th ACM Conference on Hypertext and Social Media / Sep 12, 2019

Beigi, G., Shu, K., Guo, R., Wang, S., & Liu, H. (2019). Privacy Preserving Text Representation Learning. Proceedings of the 30th ACM Conference on Hypertext and Social Media. https://doi.org/10.1145/3342220.3344925

Towards privacy preserving social recommendation under personalized privacy settings

World Wide Web / Jul 14, 2018

Meng, X., Wang, S., Shu, K., Li, J., Chen, B., Liu, H., & Zhang, Y. (2018). Towards privacy preserving social recommendation under personalized privacy settings. World Wide Web, 22(6), 2853–2881. https://doi.org/10.1007/s11280-018-0620-z

Random-Forest-Inspired Neural Networks

ACM Transactions on Intelligent Systems and Technology / Oct 29, 2018

Wang, S., Aggarwal, C., & Liu, H. (2018). Random-Forest-Inspired Neural Networks. ACM Transactions on Intelligent Systems and Technology, 9(6), 1–25. https://doi.org/10.1145/3232230

Paired Restricted Boltzmann Machine for Linked Data

Proceedings of the 25th ACM International on Conference on Information and Knowledge Management / Oct 24, 2016

Wang, S., Tang, J., Morstatter, F., & Liu, H. (2016). Paired Restricted Boltzmann Machine for Linked Data. Proceedings of the 25th ACM International on Conference on Information and Knowledge Management. https://doi.org/10.1145/2983323.2983756

Deep Multi-Graph Clustering via Attentive Cross-Graph Association

Proceedings of the 13th International Conference on Web Search and Data Mining / Jan 20, 2020

Luo, D., Ni, J., Wang, S., Bian, Y., Yu, X., & Zhang, X. (2020). Deep Multi-Graph Clustering via Attentive Cross-Graph Association. Proceedings of the 13th International Conference on Web Search and Data Mining. https://doi.org/10.1145/3336191.3371806

Exploiting Emotion on Reviews for Recommender Systems

Proceedings of the AAAI Conference on Artificial Intelligence / Apr 29, 2018

Meng, X., Wang, S., Liu, H., & Zhang, Y. (2018). Exploiting Emotion on Reviews for Recommender Systems. Proceedings of the AAAI Conference on Artificial Intelligence, 32(1). https://doi.org/10.1609/aaai.v32i1.11685

Linked Variational AutoEncoders for Inferring Substitutable and Supplementary Items

Proceedings of the Twelfth ACM International Conference on Web Search and Data Mining / Jan 30, 2019

Rakesh, V., Wang, S., Shu, K., & Liu, H. (2019). Linked Variational AutoEncoders for Inferring Substitutable and Supplementary Items. Proceedings of the Twelfth ACM International Conference on Web Search and Data Mining. https://doi.org/10.1145/3289600.3290963

Facilitating Time Critical Information Seeking in Social Media

IEEE Transactions on Knowledge and Data Engineering / Oct 01, 2017

Ranganath, S., Wang, S., Hu, X., Tang, J., & Liu, H. (2017). Facilitating Time Critical Information Seeking in Social Media. IEEE Transactions on Knowledge and Data Engineering, 29(10), 2197–2209. https://doi.org/10.1109/tkde.2017.2701375

Predicting Online Protest Participation of Social Media Users

Proceedings of the AAAI Conference on Artificial Intelligence / Feb 21, 2016

Ranganath, S., Morstatter, F., Hu, X., Tang, J., Wang, S., & Liu, H. (2016). Predicting Online Protest Participation of Social Media Users. Proceedings of the AAAI Conference on Artificial Intelligence, 30(1). https://doi.org/10.1609/aaai.v30i1.9988

Times series forecasting for urban building energy consumption based on graph convolutional network

Applied Energy / Feb 01, 2022

Hu, Y., Cheng, X., Wang, S., Chen, J., Zhao, T., & Dai, E. (2022). Times series forecasting for urban building energy consumption based on graph convolutional network. Applied Energy, 307, 118231. https://doi.org/10.1016/j.apenergy.2021.118231

Towards Robust Graph Neural Networks for Noisy Graphs with Sparse Labels

Proceedings of the Fifteenth ACM International Conference on Web Search and Data Mining / Feb 11, 2022

Dai, E., Jin, W., Liu, H., & Wang, S. (2022). Towards Robust Graph Neural Networks for Noisy Graphs with Sparse Labels. Proceedings of the Fifteenth ACM International Conference on Web Search and Data Mining. https://doi.org/10.1145/3488560.3498408

Semi-Supervised Graph-to-Graph Translation

Proceedings of the 29th ACM International Conference on Information & Knowledge Management / Oct 19, 2020

Zhao, T., Tang, X., Zhang, X., & Wang, S. (2020). Semi-Supervised Graph-to-Graph Translation. Proceedings of the 29th ACM International Conference on Information & Knowledge Management. https://doi.org/10.1145/3340531.3411977

Identifying Rhetorical Questions in Social Media

Proceedings of the International AAAI Conference on Web and Social Media / Aug 04, 2021

Ranganath, S., Hu, X., Tang, J., Wang, S., & Liu, H. (2021). Identifying Rhetorical Questions in Social Media. Proceedings of the International AAAI Conference on Web and Social Media, 10(1), 667–670. https://doi.org/10.1609/icwsm.v10i1.14771

Towards Fair Classifiers Without Sensitive Attributes

Proceedings of the Fifteenth ACM International Conference on Web Search and Data Mining / Feb 11, 2022

Zhao, T., Dai, E., Shu, K., & Wang, S. (2022). Towards Fair Classifiers Without Sensitive Attributes. Proceedings of the Fifteenth ACM International Conference on Web Search and Data Mining. https://doi.org/10.1145/3488560.3498493

Opinions Power Opinions: Joint Link and Interaction Polarity Predictions in Signed Networks

2018 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM) / Aug 01, 2018

Derr, T., Wang, Z., & Tang, J. (2018). Opinions Power Opinions: Joint Link and Interaction Polarity Predictions in Signed Networks. 2018 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM). https://doi.org/10.1109/asonam.2018.8508263

Popularity prediction on vacation rental websites

Neurocomputing / Oct 01, 2020

Li, Y., Wang, S., Ma, Y., Pan, Q., & Cambria, E. (2020). Popularity prediction on vacation rental websites. Neurocomputing, 412, 372–380. https://doi.org/10.1016/j.neucom.2020.05.092

Understanding and Identifying Rhetorical Questions in Social Media

ACM Transactions on Intelligent Systems and Technology / Jan 10, 2018

Ranganath, S., Hu, X., Tang, J., Wang, S., & Liu, H. (2018). Understanding and Identifying Rhetorical Questions in Social Media. ACM Transactions on Intelligent Systems and Technology, 9(2), 1–22. https://doi.org/10.1145/3108364

Attacking Black-box Recommendations via Copying Cross-domain User Profiles

2021 IEEE 37th International Conference on Data Engineering (ICDE) / Apr 01, 2021

Fan, W., Derr, T., Zhao, X., Ma, Y., Liu, H., Wang, J., Tang, J., & Li, Q. (2021). Attacking Black-box Recommendations via Copying Cross-domain User Profiles. 2021 IEEE 37th International Conference on Data Engineering (ICDE). https://doi.org/10.1109/icde51399.2021.00140

Exploring Edge Disentanglement for Node Classification

Proceedings of the ACM Web Conference 2022 / Apr 25, 2022

Zhao, T., Zhang, X., & Wang, S. (2022). Exploring Edge Disentanglement for Node Classification. Proceedings of the ACM Web Conference 2022. https://doi.org/10.1145/3485447.3511929

Semi-supervised anomaly detection in dynamic communication networks

Information Sciences / Sep 01, 2021

Meng, X., Wang, S., Liang, Z., Yao, D., Zhou, J., & Zhang, Y. (2021). Semi-supervised anomaly detection in dynamic communication networks. Information Sciences, 571, 527–542. https://doi.org/10.1016/j.ins.2021.04.056

Exploiting Hierarchical Structures for Unsupervised Feature Selection

Proceedings of the 2017 SIAM International Conference on Data Mining / Jun 09, 2017

Wang, S., Wang, Y., Tang, J., Aggarwal, C., Ranganath, S., & Liu, H. (2017). Exploiting Hierarchical Structures for Unsupervised Feature Selection. Proceedings of the 2017 SIAM International Conference on Data Mining, 507–515. https://doi.org/10.1137/1.9781611974973.57

Weakly Supervised Facial Attribute Manipulation via Deep Adversarial Network

2018 IEEE Winter Conference on Applications of Computer Vision (WACV) / Mar 01, 2018

Wang, Y., Wang, S., Qi, G., Tang, J., & Li, B. (2018). Weakly Supervised Facial Attribute Manipulation via Deep Adversarial Network. 2018 IEEE Winter Conference on Applications of Computer Vision (WACV). https://doi.org/10.1109/wacv.2018.00019

Towards Unbiased and Robust Causal Ranking for Recommender Systems

Proceedings of the Fifteenth ACM International Conference on Web Search and Data Mining / Feb 11, 2022

Xiao, T., & Wang, S. (2022). Towards Unbiased and Robust Causal Ranking for Recommender Systems. Proceedings of the Fifteenth ACM International Conference on Web Search and Data Mining. https://doi.org/10.1145/3488560.3498521

Randomized Feature Engineering as a Fast and Accurate Alternative to Kernel Methods

Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining / Aug 04, 2017

Wang, S., Aggarwal, C., & Liu, H. (2017). Randomized Feature Engineering as a Fast and Accurate Alternative to Kernel Methods. Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. https://doi.org/10.1145/3097983.3098001

Reconstruction-based Unsupervised Feature Selection: An Embedded Approach

Proceedings of the Twenty-Sixth International Joint Conference on Artificial Intelligence / Aug 01, 2017

Li, J., Tang, J., & Liu, H. (2017). Reconstruction-based Unsupervised Feature Selection: An Embedded Approach. Proceedings of the Twenty-Sixth International Joint Conference on Artificial Intelligence. https://doi.org/10.24963/ijcai.2017/300

Price Recommendation on Vacation Rental Websites

Proceedings of the 2017 SIAM International Conference on Data Mining / Jun 09, 2017

Li, Y., Wang, S., Yang, T., Pan, Q., & Tang, J. (2017). Price Recommendation on Vacation Rental Websites. Proceedings of the 2017 SIAM International Conference on Data Mining, 399–407. https://doi.org/10.1137/1.9781611974973.45

Self-Supervised learning for Conversational Recommendation

Information Processing & Management / Nov 01, 2022

Li, S., Xie, R., Zhu, Y., Zhuang, F., Tang, Z., Zhao, W. X., & He, Q. (2022). Self-Supervised learning for Conversational Recommendation. Information Processing & Management, 59(6), 103067. https://doi.org/10.1016/j.ipm.2022.103067

HP-GMN: Graph Memory Networks for Heterophilous Graphs

2022 IEEE International Conference on Data Mining (ICDM) / Nov 01, 2022

Xu, J., Dai, E., Zhang, X., & Wang, S. (2022). HP-GMN: Graph Memory Networks for Heterophilous Graphs. 2022 IEEE International Conference on Data Mining (ICDM). https://doi.org/10.1109/icdm54844.2022.00165

Global-and-Local Aware Data Generation for the Class Imbalance Problem

Proceedings of the 2020 SIAM International Conference on Data Mining / Jan 01, 2020

Wang, W., Wang, S., Fan, W., Liu, Z., & Tang, J. (2020). Global-and-Local Aware Data Generation for the Class Imbalance Problem. Proceedings of the 2020 SIAM International Conference on Data Mining, 307–315. https://doi.org/10.1137/1.9781611976236.35

Investigating and Mitigating Degree-Related Biases in Graph Convoltuional Networks

Proceedings of the 29th ACM International Conference on Information & Knowledge Management / Oct 19, 2020

Tang, X., Yao, H., Sun, Y., Wang, Y., Tang, J., Aggarwal, C., Mitra, P., & Wang, S. (2020). Investigating and Mitigating Degree-Related Biases in Graph Convoltuional Networks. Proceedings of the 29th ACM International Conference on Information & Knowledge Management. https://doi.org/10.1145/3340531.3411872

Graph Convolutional Networks with EigenPooling

Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining / Jul 25, 2019

Ma, Y., Wang, S., Aggarwal, C. C., & Tang, J. (2019). Graph Convolutional Networks with EigenPooling. Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining. https://doi.org/10.1145/3292500.3330982

Multi-dimensional Graph Convolutional Networks

Proceedings of the 2019 SIAM International Conference on Data Mining / May 06, 2019

Ma, Y., Wang, S., Aggarwal, C. C., Yin, D., & Tang, J. (2019). Multi-dimensional Graph Convolutional Networks. Proceedings of the 2019 SIAM International Conference on Data Mining, 657–665. https://doi.org/10.1137/1.9781611975673.74

Links & Social Media

Research Interests

Machine learning
Graph Mining
General Medicine
General Computer Science
Theoretical Computer Science
Information Systems and Management
Computer Science Applications
Information Systems
General Physics and Astronomy
Cognitive Neuroscience
Computer Vision and Pattern Recognition
Artificial Intelligence
Control and Systems Engineering
Software
Multidisciplinary
Management Information Systems
Computational Theory and Mathematics
Computer Networks and Communications
Hardware and Architecture
Management, Monitoring, Policy and Law
Mechanical Engineering
General Energy
Building and Construction
Library and Information Sciences
Management Science and Operations Research
Media Technology
Join Suhang on NotedSource!

At NotedSource, we believe that professors, post-docs, scientists and other researchers have deep, untapped knowledge and expertise that can be leveraged to drive innovation within companies. NotedSource is committed to bridging the gap between academia and industry by providing a platform for collaboration with industry and networking with other researchers.

For industry, NotedSource identifies the right academic experts in 24 hours to help organizations build and grow. With a platform of thousands of knowledgeable PhDs, scientists, and industry experts, NotedSource makes connecting and collaborating easy.

For academic researchers such as professors, post-docs, and Ph.D.s, NotedSource provides tools to discover and connect to your colleagues with messaging and news feeds, in addition to the opportunity to be paid for your collaboration with vetted partners.

Expert Institutions
NotedSource has experts from Stanford University
Expert institutions using NotedSource include Oxfort University
Experts from McGill have used NotedSource to share their expertise
University of Chicago experts have used NotedSource
MIT researchers have used NotedSource
Proudly trusted by
Microsoft uses NotedSource for academic partnerships
Johnson & Johnson academic research projects on NotedSource
ProQuest (Clarivate) uses NotedSource as their industry academia platform
Slamom consulting engages academics for research collaboration on NotedSource
Omnicom and OMG find academics on notedsource
Phoenix Tailings finds academic collaborations on NotedSource
Unilever research project have used NotedSource to engage academic experts

Connect with researchers and scientists like Suhang Wang on NotedSource to help your company with innovation, research, R&D, L&D, and more.