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DIAL Publications

2020

  1. Zhichun Guo, Wenhau Yu, Chuxu Zhang, Meng Jiang, Nitesh V. Chawla. “GraSeq: Graph and Sequence Fusion Learning for Molecular Property Prediction.” Proceedings of the 29th ACM International Conference on Information and Knowledge Management 2020 (CIKM’ 2020) PDF
  2. Steven J. Krieg, Daniel H. Robertson, Meeta P. Pradhan, Nitesh V. Chawla. “Higher-order Networks of Diabetes Comorbidities: Disease Trajectories that Matter.” 2020 IEEE International Conference on Healthcare Informatics (ICHI) PDF
  3. Steven J. Krieg, Peter M. Kogge, Nitesh V. Chawla. “GrowHON: A Scalable Algorithm for Growing Higher-order Networks of Sequences.” 2020 International Conference on Complex Networks and Their Applications PDF
  4. Jennifer J. Schnur, Ryan Karl, Angélica García-Martinez, Meng Jiang, Nitesh V. Chawla. “Imputing Growth Snapshot Similarity in Early Childhood Development: A Tensor Decomposition Approach.” 2020 IEEE International Conference on Bioinformatics and Biomedicine (BIBM) PDF
  5. Mandana Saebi, Jian Xu, Salvatore R. Curasi, Erin K. Grey, Nitesh V. Chawla, David M. Lodge. “Network analysis of ballast-mediated species transfer reveals important introduction and dispersal patterns in the Arctic.” Scientific Reports PDF
  6. Mandana Saebi, Giovanni Luca Ciampaglia, Lance M. Kaplan, Nitesh V. Chawla. “HONEM: Learning Embedding for Higher Order Networks.” Big Data arXiv
  7. Saebi, Mandana, Jian Xu, Lance M. Kaplan, Bruno Ribeiro, and Nitesh V. Chawla. “Efficient Modeling of Higher-order Dependencies in Networks: From Algorithm to Application for Anomaly Detection.” EPJ Data Science 9, no. 1 (2020): 1-22. PDF
  8. Saebi, Mandana, Jian Xu, Erin K. Grey, David M. Lodge, James J. Corbett, and Nitesh Chawla.”Higher-order Patterns of Aquatic Species Spread through the Global Shipping Network.” PLOS ONE15, no. 7 (2020): e0220353. PDF
  9. Daheng Wang, Meng Jiang, Munira Syed, Oliver Conway, Vishal Juneja, Sriram Subramanian, Nitesh V. Chawla.”Calendar Graph Neural Networks for Modeling Time Structures in Spatiotemporal User Behaviors”. Proceedings of the 26th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, 2020. PDF
  10. Wang, Daheng, Zhihan Zhang, Yihong Ma, Tong Zhao, Tianwen Jiang, Nitesh V. Chawla, and Meng Jiang. 2020. “Learning Attribute-Structure Co-Evolutions in Dynamic Graphs.” ArXiv. Republished from The Second International Workshop on Deep Learning on Graphs: Methods and Applications (DLG-KDD’20), Best Paper Award of DLG-KDD’20. PDF
  11. Steven J. Krieg, Jennifer J. Schnur, Jermaine D. Marshall, Matthew M. Schoenbauer, and Nitesh V. Chawla. Pandemic Pulse: Unraveling and Modeling Social Signals During the COVID-19 Pandemic. Digital Government 2, no. 2. 2020: 9 pages. PDF

2019

  1. Daheng Wang, Tianwen Jiang, Nitesh V. Chawla, Meng Jiang. “TUBE: Embedding Behavior Outcomes for Predicting Success.” Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining PDF
  2. Chuxu Zhang, Dongjin Song, Chao Huang, Ananthram Swami, Nitesh V. Chawla. “Heterogeneous Graph Neural Network.” 25th ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD) PDF
  3. Munira Syed, Jermaine Marshall, Aastha Nigam, Nitesh V. Chawla. “Gender Prediction Through Synthetic Resampling of User Profiles Using SeqGANs.” International Conference on Computational Data and Social Networks (CSoNet), 2019 PDF
  4. Munira Syed, Malolan Chetlur, Shazia Afzal, G. Alex Ambros, Nitesh V. Chawla. “Implicit and Explicit Emotions in MOOCs.” Educational Data Mining (EDM) PDF
  5. Munira Syed, Trunojoyo Anggara, Alison Lanski, Xiaojing Duan, G. Alex Ambrose, Nitesh V. Chawla. “Integrated Closed-loop Learning Analytics Scheme in a First Year Experience Course.” Learning Analytics and Knowledge (LAK) PDF
  6. Suwen Lin, Louis Faust, Pablo Robles-Granda, Tomasz Kajdanowicz, Nitesh V. Chawla. “Social Network Structure is Predictive of Health and Wellness.” PLOS ONE PDF
  7. Louis Faust, Keith Feldman, Nitesh V. Chawla. “Examining the Weekend Effect Across ICU Performance Metrics.” BMC Critical Care PDF
  8. Louis Faust, Priscilla Jiménez-Pazmino, James K. Holland, Omar Lizardo, David Hachen, Nitesh V. Chawla. “What 30 Days Tells Us About 3 Years: Identifying Early Signs of User Abandonment and Non-Adherence.” Proceedings of the 13th EAI International Conference on Pervasive Computing PDF
  9. Louis Faust, Cheng Wang, David Hachen, Omar Lizardo, Nitesh V. Chawla. “PATX: A Framework for Extracting Moderate-Vigorous Physical Activity Trends From Wearable Fitness Tracker Data.” JMIR mHealth and uHealth PDF
  10. Beenish Chaudhry, Louis Faust, Nitesh V. Chawla. “Development and Evaluation of a Web Application for Prenatal Care Coordinators in the United States.” International Conference on Design Science Research in Information Systems PDF
  11. Frederick Nwanganga, Nitesh V. Chawla, Gregory Madey. “Statistical Analysis and Modeling of Heterogeneous Workloads on Amazon’s Public Cloud Infrastructure.” Proceedings of the 52nd Hawaii International Conference on System Sciences PDF
  12. Aastha Nigam, Reid Johnson, Dong Wang, Nitesh V. Chawla. “Characterizing online health and wellness information consumption: A study.” Information Fusion PDF
  13. Jun Tao, Martin Imre, Chaoli Wang, Nitesh V. Chawla, Hanqi Guo, Gökhan Sever, Seung Hyun Kim . “Exploring Time-Varying Multivariate Volume Data Using Matrix of Isosurface Similarity Maps.” IEEE Transactions on Visualization and Computer Graphics PDF
  14. Chuxu Zhang, Ananthram Swami, Nitesh V. Chawla. “SHNE: Representation Learning for Semantic-Associated Heterogeneous Networks.” The 12th ACM International Conference on Web Search and Data Mining (WSDM 2019) PDF
  15. Chuxu Zhang, Dongjin Song, Yuncong Chen, Xinyang Feng, Cristian Lumezanu, Wei Cheng, Jingchao Ni, Bo Zong, Haifeng Chen, Nitesh V. Chawla. “A Deep Neural Network for Unsupervised Anomaly Detection and Diagnosis in Multivariate Time Series Data.” The 33rd Conference on Artificial Intelligence (AAAI 2019) PDF
  16. Xian Wu, Baoxu Shi, Yuxiao Dong, Chao Huang, Nitesh V. Chawla. “Neural Tensor Factorization for Temporal Interaction Learning.” The 12th ACM International Conference on Web Search and Data Mining (WSDM 2019)PDF
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