Publications

In chronological order | Google Scholar | DBLP

Preprints

• [P10] A Survey of Knowledge-Enhanced Text Generation by W. Yu, C. Zhu, Z. Li, Z. Hu, Q. Wang, H. Ji, M. Jiang. arXiv preprint arXiv:2010.04389, October 2020.
• [P9] Data-Driven Network Intrusion Detection: A Taxonomy of Challenges and Methods by D. Chou, M. Jiang. arXiv preprint arXiv:2009.07352, September 2020.
• [P8] Injecting Entity Types into Entity-Guided News Generation by X. Dong, W. Yu, C. Zhu, M. Jiang. arXiv preprint arXiv:2009.13401, September 2020.
• [P7] Federated Dynamic GNN with Secure Aggregation by M. Jiang, T. Jung, R. Karl, T. Zhao. arXiv preprint arXiv:2009.07351, September 2020.
• [P6] Canonicalizing Open Knowledge Bases with Multi-Layered Meta-Graph Neural Network by T. Jiang, T. Zhao, B. Qin, T. Liu, N.V. Chawla, M. Jiang. arXiv preprint arXiv:2006.09610, June 2020.
• [P5] Data Augmentation for Graph Neural Networks by T. Zhao, Y. Liu, L. Neves, O. Woodford, M. Jiang, N. Shah. arXiv preprint arXiv:2006.06830, June 2020.
• [P4] Boosting Factual Correctness of Abstractive Summarization with Knowledge Graph by C. Zhu, W. Hinthorn, R. Xu, Q. Zeng, M. Zeng, X. Huang, M. Jiang. arXiv preprint arXiv:2003.08612, March 2020.
• [P3] Heterogeneous Relational Reasoning in Knowledge Graphs with Reinforcement Learning by M. Saebi, S. Krieg, C. Zhang, M. Jiang, N.V. Chawla. arXiv preprint arXiv:2003.06050, March 2020.
• [P2] Improving Generalizability of Fake News Detection Methods using Propensity Score Matching by B. Ni, Z. Guo, J. Li, M. Jiang. arXiv preprint arXiv:2002.00838, January 2020.
• [P1] Contrast Subgraph Mining from Coherent Cores by J. Shang, X. Shi, M. Jiang, L. Liu, T. Hanratty, J. Han. arXiv preprint arXiv:1802.06189, February 2018.

Journal Papers

• [J21] Structural and Textual Information Fusion for Symptom and Disease Representation Learning by S. Zhao, M. Jiang, B. Qin, T. Liu, C.X. Zhai, F. Wang. IEEE Transactions on Knowledge and Data Engineering (TKDE), 2020. (In press) [DOI]
• [J20] Biomedical Knowledge Graphs Construction from Conditional Statements by T. Jiang, Q. Zeng, T. Zhao, B. Qin, T. Liu, N. Chawla, M. Jiang. IEEE/ACM Transactions on Computational Biology and Bioinformatics (TCBB), 2020. [DOI] [bibtex]
• [J19] Binarizing Weights Wisely for Edge Intelligence: Guide for Partial Binarization of Deconvolution-Based Generators by J. Liu, X. Xu, M. Jiang, Y. Shi. IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems (TCADICS), 2020. [DOI] [bibtex]
• [J18] Leverage Electron Properties to Predict Phonon Properties via Transfer Learning for Semiconductors by Z. Liu, M. Jiang, T. Luo. Science Advances, 2020. (IF=12.530) [DOI]
• [J17] Combined Heat and Power System Intelligent Economic Dispatch: A Deep Reinforcement Learning Approach by S. Zhou, Z. Hu, W. Gu, M. Jiang, M. Chen, Q. Hong, C. Booth. International Journal of Electrical Power and Energy System, 2020. [DOI] [bibtex]
• [J16] Precise Temporal Slot Filling via Truth Finding with Data-Driven Commonsense by X. Wang, M. Jiang. Knowledge and Information Systems, 2020. [DOI] [bibtex]
• [J15] Phrase-level Pairwise Topic Modeling to Uncover Helpful Peer Responses to Online Suicidal Crises by M. Jiang, B.A. Ammerman, Q. Zeng, R. Jacobucci, A. Brodersen. Nature Humanities and Social Sciences Communications, 2020. [DOI] [bibtex]
• [J14] Treatment Effect Estimation via Differentiated Confounder Balancing and Regression by K. Kuang, P. Cui, B. Li, M. Jiang, S. Yang. Transactions on Knowledge Discovery from Data (TKDD), 2019. [DOI] [bibtex]
• [J13] An Integrated Energy System Operating Scenarios Generator based on Generative Adversarial Network by S. Zhou, Z. Hu, Z. Zhong, D. He, M. Jiang. Sustainability, 2019. [DOI] [bibtex]
• [J12] Artificial Intelligence based Smart Energy Community Management: A Reinforcement Learning Approach by S. Zhou, Z. Hu, W. Gu, M. Jiang, X. Zhang. CSEE Journal of Power and Energy Systems, 2019. [DOI] [bibtex]
• [J11] Effective Promotional Strategies Selection in Social Media: A Data-Driven Approach by K. Kuang, M. Jiang, P. Cui, J. Sun, S. Yang. IEEE Transactions on Big Data (TBD), 2018. [DOI] [bibtex]
• [J10] Automated Phrase Mining from Massive Text Corpora by J. Shang, J. Liu, M. Jiang, X. Ren, C. R. Voss, J. Han. IEEE Transactions on Knowledge and Data Engineering (TKDE), 2018. [DOI] [bibtex]
• [J9] DPPred: An Effective Prediction Framework with Concise Discriminative Patterns by J. Shang, M. Jiang, W. Tong, J. Xiao, J. Peng, J. Han. IEEE Transactions on Knowledge and Data Engineering (TKDE), 2018. [DOI] [bibtex]
• [J8] Embedding Learning with Events in Heterogeneous Information Networks by H. Gui, J. Liu, F. Tao, M. Jiang, B. Norick, L. Kaplan, J. Han. IEEE Transactions on Knowledge and Data Engineering (TKDE), 2017. [DOI] [bibtex]
• [J7] Spotting Suspicious Behaviors in Multimodal Data: A General Metric and Algorithms by M. Jiang, A. Beutel, P. Cui, B. Hooi, S. Yang, C. Faloutsos. IEEE Transactions on Knowledge and Data Engineering (TKDE), 2016. [DOI] [bibtex]
• [J6] Suspicious Behavior Detection: Current Trends and Future Directions by M. Jiang, P. Cui, C. Faloutsos. Special Issue on Online Behavioral Analysis and Modeling, IEEE Intelligent Systems (IS), 2016. [DOI] [bibtex]
• [J5] Catching Synchronized Behaviors in Large Networks: A Graph Mining Approach by M. Jiang, P. Cui, A. Beutel, C. Faloutsos, S. Yang. Special Issue on the Best of SIGKDD 2014, ACM Transactions on Knowledge Discovery from Data (TKDD), 2016. [DOI] [bibtex]
• [J4] Inferring Lockstep Behavior from Connectivity Pattern in Large Graphs by M. Jiang, P. Cui, A. Beutel, C. Faloutsos, S. Yang. Knowledge and Information Systems (KAIS), 2016. [DOI] [bibtex]
• [J3] Social Recommendation with Cross-Domain Transferable Knowledge by M. Jiang, P. Cui, X. Chen, F. Wang, W. Zhu, S. Yang. IEEE Transactions on Knowledge and Data Engineering (TKDE), 2015. [DOI] [bibtex]
• [J2] Scalable Recommendation with Social Contextual Information by M. Jiang, P. Cui, F. Wang, W. Zhu, S. Yang. IEEE Transactions on Knowledge and Data Engineering (TKDE), 2014. [DOI] [bibtex]
• [J1] Mining Diversity on Social Media Networks by L. Liu, F. Zhu, M. Jiang, J. Han, L. Sun, S. Yang. Multimedia Tools and Applications (MTA), 2012. [DOI] [bibtex]

Conference Papers

• [C50] Imputing Growth Snapshot Similarity in Early Childhood Development: A Tensor Decomposition Approach by J. Schnur, R. Karl, A. Garcia-Martinez, M. Jiang, N.V. Chawla. IEEE International Conference on Bioinformatics and Biomedicine (BIBM), 2020.
• [C49] Use of Internal Knowledge: Biomedical Literature Search Liberated From External Resources by T. Jiang, N. Zhang, M. Liu, M. Jiang, T. Liu, B. Qin. IEEE International Conference on Bioinformatics and Biomedicine (BIBM), 2020.
• [C48] Overcoming Data Sparsity in Predicting User Characteristics from Behavior through Graph Embeddings by M. Syed, D. Wang, M. Jiang, O. Conway, V. Juneja, S. Subramanian, N.V. Chawla. IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM), 2020.
• [C47] A Technical Question Answering System with Transfer Learning by W. Yu, L. Wu, Y. Deng, R. Mahindru, Q. Zeng, S. Guven, M. Jiang. Findings of Empirical Methods on Natural Language Processing (EMNLP), 2020. (demo) [github] [video]
• [C46] Tri-Train: Automatic Pre-fine Tuning between Pre-training and Fine-tune Training for SciNER by Q. Zeng, W. Yu, M. Yu, T. Jiang, T. Weninger, M. Jiang. Findings of Empirical Methods on Natural Language Processing (EMNLP), 2020.
• [C45] Few-Shot Multi-Hop Relation Reasoning over Knowledge Bases by C. Zhang, L. Yu, M. Saebi, M. Jiang, N. Chawla. Findings of Empirical Methods on Natural Language Processing (EMNLP), 2020.
• [C44] GraSeq: Graph and Sequence Fusion Learning for Molecular Property Prediction by Z. Guo, W. Yu, C. Zhang, M. Jiang, N. Chawla. ACM International Conference on Information and Knowledge Management (CIKM), 2020. [DOI]
• [C43] Error-bounded Graph Anomaly Loss for GNNs by T. Zhao, C. Deng, K. Yu, T. Jiang, D. Wang, M. Jiang. ACM International Conference on Information and Knowledge Management (CIKM), 2020. [DOI]
• [C42] Towards Semantically Guided Traceability by Y. Liu, J. Lin, Q. Zeng, M. Jiang, J. Cleland-Huang. IEEE International Requirements Engineering Conference (RE), 2020.
• [C41] Calendar Graph Neural Networks for Modeling Time Structures in Spatiotemporal User Behaviors by D. Wang, M. Jiang, M. Syed, O. Conway, V. Juneja, S. Subramanian, N. Chawla. ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD), 2020. [DOI]
• [C40] Crossing Variational Autoencoders for Answer Retrieval by W. Yu, L. Wu, Q. Zeng, S. Tao, Y. Deng, M. Jiang. Annual Meeting of the Association for Computational Linguistics (ACL), 2020. [DOI] [bibtex]
• [C39] Improving Situational Awareness with Collective Artificial Intelligence over Knowledge Graphs by M. Jiang. Artificial Intelligence and Machine Learning for Multi-Domain Operations Applications II in SPIE Defense + Commercial Sensing Symposium, 2020. [DOI] [bibtex]
• [C38] Identifying Referential Intention with Heterogeneous Contexts by W. Yu, M. Yu, T. Zhao, M. Jiang. The Web Conference (TheWebConf), 2020. [DOI] [bibtex]
• [C37] Experimental Evidence Extraction in Data Science with Hybrid Table Features and Ensemble Learning by W. Yu, W. Peng, Y. Shu, Q. Zeng, M. Jiang. The Web Conference (TheWebConf), 2020. [DOI] [bibtex]
• [C36] Multi-label Patent Categorization with Non-local Attention-based Graph Convolutional Network by P. Tang, M. Jiang, N. Xia, J. Pitera, J. Welser, N. Chawla. AAAI Conference on Artificial Intelligence (AAAI), 2020.
• [C35] Few-Shot Knowledge Base Completion by C. Zhang, H. Yao, M. Jiang, Z. Li, N. Chawla. AAAI Conference on Artificial Intelligence (AAAI), 2020.
• [C34] Graph Few-shot Learning via Knowledge Transfer by H. Yao, C. Zhang, Y. Wei, M. Jiang, S. Wang, N. Chawla, Z. Li. AAAI Conference on Artificial Intelligence (AAAI), 2020.
• [C33] Preserving Composition and Crystal Structure Information of Chemical Compounds in Atomic Embedding by Y. Ding, D. Wang, T. Weninger, M. Jiang. IEEE International Conference on Big Data (BigData), 2019. (poster) [DOI] [bibtex]
• [C32] CTGA: Graph-based Biomedical Literature Search by T. Jiang, Z. Zhang, T. Zhao, B. Qin, T. Liu, N. Chawla, M. Jiang. IEEE International Conference on Bioinformatics and Biomedicine (BIBM), 2019. [DOI] [bibtex]
• [C31] Multi-input Multi-output Sequence Labeling for Joint Extraction of Fact and Condition Tuples from Scientific Text by T. Jiang, T. Zhao, B. Qin, T. Liu, N. Chawla, M. Jiang. Conference on Empirical Methods in Natural Language Processing (EMNLP), 2019. [DOI] [bibtex]
• [C30] MSU-Net: Multiscale Statistical U-Net forReal-time 3D Cardiac MRI Video Segmentation by T. Wang, X. Xu, J. Xiong, M. Jiang, Y. Shi. International Conference on Medical Image Computing and Computer Assisted Intervention (MICCAI), 2019.
• [C29] The Role of 'Condition': A Novel Scientific Knowledge Graph Representation and Construction Model by T. Jiang, T. Zhao, B. Qin, T. Liu, N. Chawla, M. Jiang. ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD), 2019. [DOI] [bibtex]
• [C28] TUBE: Embedding Behavior Outcomes for Predicting Success by D. Wang, T. Jiang, N. Chawla, M. Jiang. ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD), 2019. [DOI] [bibtex]
• [C27] Tablepedia: Automating PDF Table Reading in an Experimental Evidence Exploration and Analytic System by W. Yu, Z. Li, Q. Zeng, M. Jiang. The Web Conference (TheWebConf), 2019. (demo) [DOI] [bibtex]
• [C26] A Novel Unsupervised Approach for Precise Temporal Slot Filling from Incomplete and Noisy Temporal Contexts by X. Wang, H. Zhang, Q. Li, Y. Shi, M. Jiang. The Web Conference (TheWebConf), 2019. [DOI] [bibtex]
• [C25] Actionable Objective Optimization for Suspicious Behavior Detection on Large Bipartite Graphs by T. Zhao, M. Malir, M. Jiang. IEEE International Conference on Big Data (BigData), 2018. [DOI] [bibtex]
• [C24] Doc2Cube: Allocating Documents to Text Cube without Labeled Data by F. Tao, C. Zhang, X. Chen, M. Jiang, T. Hanratty, L. Kaplan, J. Han. IEEE International Conference on Data Mining (ICDM), 2018. [DOI] [bibtex]
• [C23] Optimizing Boiler Control in Real-Time with Machine Learning for Sustainability by Y. Ding, J. Liu, J. Xiong, M. Jiang, Y. Shi. ACM International Conference on Information and Knowledge Management (CIKM), 2018. [DOI] [bibtex]
• [C22] CausalTriad: Toward Pseudo Causal Relation Discovery and Hypotheses Generation from Medical Text Data by S. Zhao, M. Jiang, M. Liu, B. Qin, T. Liu. ACM International Conference on Bioinformatics, Computational Biology, and Health Informatics (BCB), 2018. [DOI] [bibtex]
• [C21] A Project Showcase for Planning Research Work towards Publishable Success by D. Wang, M. Jiang, X. Wang, T. Zhao, Q. Zeng, N.V. Chawla. ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD), 2018. (demo) [DOI] [bibtex]
• [C20] TaxoGen: Constructing Topical Concept Taxonomy by Adaptive Term Embedding and Clustering by C. Zhang, F. Tao, X. Chen, J. Shen, M. Jiang, B. Sadler, M. Vanni, J. Han. ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD), 2018. [DOI] [bibtex]
• [C19] TruePIE: Discovering Reliable Patterns in Pattern-Based Information Extraction by M. Jiang*, Q. Li*, X. Zhang, M. Qu, T. Hanratty, J. Gao, J. Han. ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD), 2018. (* for equal contribution) [DOI] [bibtex]
• [C18] Multi-Type Itemset Embedding for Learning Behavior Success by D. Wang, M. Jiang, Q. Zeng, Z. Eberhart, N.V. Chawla. ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD), 2018. [DOI] [bibtex]
• [C17] LifeNet: A Structured Network-Based Knowledge Exploration and Analytics System for Life Sciences by X. Ren, J. Shen, M. Qu, X. Wang, Z. Wu, Q. Zhu, M. Jiang, F. Tao, S. Sinha, D. Liem, P. Ping, R. Weinshilboum, J. Han. Annual Meeting of the Association for Computational Linguistics (ACL), 2017. (demo)
• [C16] MetaPAD: Meta Patten Discovery from Massive Text Corpora by M. Jiang, J. Shang, T. Cassidy, X. Ren, L. Kaplan, T. Hanratty, J. Han. ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD), 2017. [DOI] [bibtex] [poster]
• [C15] Estimating Treatment Effect in the Wild via Differentiated Confounder Balancing by K. Kuang, P. Cui, B. Li, M. Jiang, S. Yang. ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD), 2017. [DOI] [bibtex]
• [C14] ContextCare: Incorporating Contextual Information Networks to Representation Learning in Medical Forum Data by S. Zhao, M. Jiang, Q. Yuan, B. Qin, T. Liu, C.X. Zhai. International Joint Conference on Artificial Intelligence (IJCAI), 2017. [DOI] [bibtex]
• [C13] Treatment Effect Estimation with Data-Driven Variable Decomposition by K. Kuang, P. Cui, B. Li, M. Jiang, S. Yang, F. Wang. AAAI Conference on Artificial Intelligence (AAAI), 2017. [bibtex]
• [C12] Steering Social Media Promotions with Effective Strategies by K. Kuang, M. Jiang, P. Cui, S. Yang. IEEE International Conference on Data Mining (ICDM), 2016. [DOI] [bibtex]
• [C11] Large-Scale Embedding Learning in Heterogeneous Event Data by H. Gui, J. Liu, F. Tao, M. Jiang, B. Norick, J. Han. IEEE International Conference on Data Mining (ICDM), 2016. [bibtex]
• [C10] CatchTartan: Representing and Summarizing Dynamic Multicontextual Behaviors by M. Jiang, C. Faloutsos, J. Han. ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD), 2016. [DOI] [bibtex] [slides] [poster]
• [C9] Little is Much: Bridging Cross-Platform Behaviors through Overlapped Crowds by M. Jiang, P. Cui, N. J. Yuan, X. Xie, S. Yang. AAAI Conference on Artificial Intelligence (AAAI), 2016. [bibtex] [slides] [poster]
• [C8] A General Suspiciousness Metric for Dense Blocks in Multimodal Data by M. Jiang, A. Beutel, P. Cui, B. Hooi, S. Yang, C. Faloutsos. IEEE International Conference on Data Mining (ICDM), 2015. [DOI] [bibtex] [slides]
• [C7] FEMA: Flexible Evolutionary Multi-Faceted Analysis for Dynamic Behavioral Pattern Discovery by M. Jiang, P. Cui, F. Wang, X. Xu, W. Zhu, S. Yang. ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD), 2014. [DOI] [bibtex] [slides] [talk]
• [C6] CatchSync: Catching Synchronized Behavior in Large Directed Graphs by M. Jiang, P. Cui, A. Beutel, C. Faloutsos, S. Yang. ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD), 2014. (Best Paper Finalist) [DOI] [bibtex] [slides] [talk] [code] [code+data]
• [C5] Inferring Strange Behavior from Connectivity Pattern in Social Networks by M. Jiang, P. Cui, A. Beutel, C. Faloutsos, S. Yang. Pacific-Asia Conference on Knowledge Discovery and Data Mining (PAKDD), 2014. [DOI] [bibtex] [slides] [code]
• [C4] Detecting Suspicious Following Behavior in Multimillion-Node Social Networks by M. Jiang, P. Cui, A. Beutel, C. Faloutsos, S. Yang. International Conference on World Wide Web (WWW), 2014. (poster) [DOI] [bibtex] [poster]
• [C3] Social Recommendation across Multiple Relational Domains by M. Jiang, P. Cui, F. Wang, Q. Yang, W. Zhu, S. Yang. ACM International Conference on Information and Knowledge Management (CIKM), 2012. [DOI] [bibtex] [slides]
• [C2] Social Contextual Recommendation by M. Jiang, P. Cui, R. Liu, Q. Yang, F. Wang, W. Zhu, S. Yang. ACM International Conference on Information and Knowledge Management (CIKM), 2012. [DOI] [bibtex] [slides]
• [C1] Mining Topic-Level Influence in Heterogeneous Networks by L. Liu, J. Tang, J. Han, M. Jiang, S. Yang. ACM International Conference on Information and Knowledge Management (CIKM), 2010. [DOI] [bibtex]

Workshop Paper

• [W13] SmartFund: Predicting Research Outcomes with Machine Learning and Natural Language Processing by A. Alaphat, M. Jiang. International Workshop on Big Data Tools, Methods, and Use Cases for Innovative Scientific Discovery (BTSD) at IEEE International Conference on Big Data (BigData), 2020. (Regular paper, 10 pages. Mr. Alaphat was a frenshman undergraduate when he finished the work.)
• [W12] Learning Attribute-Structure Co-Evolutions in Dynamic Graphs by D. Wang, Z. Zhang, Y. Ma, T. Zhao, T. Jiang, N.V. Chawla, M. Jiang. International Workshop on Deep Learning on Graphs: Methods and Applications (DLG) at ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD), 2020. (Best Paper Award)
• [W11] Early Fraud Detection with Augmented Graph Learning by T. Zhao, N. Bo, W. Yu, M. Jiang. International Workshop on Deep Learning on Graphs: Methods and Applications (DLG) at ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD), 2020.
• [W10] GNN-based Graph Anomaly Detection with Graph Anomaly Loss by T. Zhao, C. Deng, K. Yu, T. Jiang, D. Wang, M. Jiang. International Workshop on Deep Learning on Graphs: Methods and Applications (DLG) at ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD), 2020.
• [W9] A Probabilistic Model with Commonsense Constraints for Pattern-based Temporal Fact Extraction by Y. Zhou, T. Zhao, M. Jiang. Workshop on Fact Extraction and Verification (FEVER) at Annual Meeting of the Association for Computational Linguistics (ACL), 2020.
• [W8] Generating Helpful Responses for Intelligent Tech Support by W. Yu, L. Wu, S. Tao, Y. Deng, Q. Zeng, M. Jiang. Reasoning for Complex QA Workshop (RCQA) at AAAI Conference on Artificial Intelligence (AAAI), 2020.
• [W7] Graph Few-shot Learning via Knowledge Transfer by H. Yao, C. Zhang, Y. Wei, M. Jiang, S. Wang, N.V. Chawla, Z. Li. Graph Representation Learning Workshop (GRL) at Conference on Neural Information Processing Systems (NeurIPS), 2019.
• [W6] Faceted Hierarchy: A New Graph Type to Organize Scientific Concepts and a Construction Method by Q. Zeng, M. Yu, W. Yu, J. Xiong, Y. Shi, M. Jiang. Workshop on Graph-Based Natural Language Processing (TextGraphs) at Conference on Empirical Methods in Natural Language Processing (EMNLP), 2019.
• [W5] A Study of Person Entity Extraction and Profiling from Classical Chinese Historiography by Y. Ma, Q. Zeng, T. Jiang, L. Cai, M. Jiang. International Workshop on EntitY REtrieval (EYRE) at ACM International Conference on Information and Knowledge Management (CIKM), 2019.
• [W4] Constructing Information-Lossless Biological Knowledge Graphs from Conditional Statements by T. Jiang, T. Zhao, B. Qin, T. Liu, N.V. Chawla, M. Jiang. International Workshop on Data Mining in Bioinformatics (BioKDD) at ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD), 2019.
• [W3] Multifaceted Event Analysis on Cross-Media Network Data by D. Wang, M. Jiang, X. Wang, N. Chawla, and P. Brunts. International Workshop on Heterogeneous Networks Analysis and Mining (HeteroNAM) at ACM Conference on Web Search and Data Mining (WSDM), 2018.
• [W2] Integrating Knowledge from Latent and Explicit Features for Triple Scoring—Team Radicchio's Triple Scorer at WSDM Cup 2017 by L.W. Chen, B. Mangipudi, J. Bandlamudi, R. Sehgal, Y. Hao, M. Jiang, H. Gui. The 10th ACM Conference on Web Search and Data Mining (WSDM) Notebook Papers, 2017.
• [W1] Catching Social Media Advertisers with Strategy Analysis by M. Jiang. The First International Workshop on Computational Methods for CyberSafety at ACM International Conference on Information and Knowledge Management (CIKM), 2016. [bibtex] [slides]

Book Chapters

• [B2] Behavior Modeling in Social Networks by M. Jiang. In Encyclopedia of Social Network Analysis and Mining, 2nd edition, Springer, 2017. [Work Entry] [Book]
• [B1] Mining User Behaviors in Large Social Networks by M. Jiang and P. Cui. Book Chapter in Big Data in Complex and Social Networks, Chapman and Hall/CRC Big Data Series, 2016. [CRCnetBASE] [CRC Press] [Amazon]

Online Demonstrations

• CTGA: Graph-based Biomedical Literature Search by T. Jiang, Z. Zhang, Q. Zeng, M. Jiang. 2019.
• Topic-level #SuicideWatch Reddit post-to-comment volume/vote graphs by M. Jiang. 2018.
• Ru's social networks in China's Han Dynasty by M. Jiang. 2018.
• Visualizing Heterogeneous Information Networks in DBLP Sample Data by M. Jiang. How does academic research of Tsinghua Social Media Lab evolve over time? Discovery from affiliation-author-paper-venue-word data of their publications.
• Flexible Evolutionary Multi-faceted Analysis for Dynamic Behavioral Pattern Discovery (KDD 2014) by M. Jiang, X. Xu. (1) How does academic research of Professor Jiawei Han change? (2) How do Weibo users mention (using "@") their idols (@superdan, etc.) in their tweets?