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Graph Barlow Twins: A self-supervised representation learning framework for graphs


Jun 04, 2021
Piotr Bielak, Tomasz Kajdanowicz, Nitesh V. Chawla


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DeepSMOTE: Fusing Deep Learning and SMOTE for Imbalanced Data


May 05, 2021
Damien Dablain, Bartosz Krawczyk, Nitesh V. Chawla

* 14 pages, 9 figures 

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Few-Shot Graph Learning for Molecular Property Prediction


Feb 16, 2021
Zhichun Guo, Chuxu Zhang, Wenhao Yu, John Herr, Olaf Wiest, Meng Jiang, Nitesh V. Chawla

* To appear in WWW 2021 (long paper); Code is available at https://github.com/zhichunguo/Meta-MGNN 

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AttrE2vec: Unsupervised Attributed Edge Representation Learning


Dec 29, 2020
Piotr Bielak, Tomasz Kajdanowicz, Nitesh V. Chawla


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Learning Attribute-Structure Co-Evolutions in Dynamic Graphs


Jul 25, 2020
Daheng Wang, Zhihan Zhang, Yihong Ma, Tong Zhao, Tianwen Jiang, Nitesh V. Chawla, Meng Jiang


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Canonicalizing Open Knowledge Bases with Multi-Layered Meta-Graph Neural Network


Jun 17, 2020
Tianwen Jiang, Tong Zhao, Bing Qin, Ting Liu, Nitesh V. Chawla, Meng Jiang


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Calendar Graph Neural Networks for Modeling Time Structures in Spatiotemporal User Behaviors


Jun 11, 2020
Daheng Wang, Meng Jiang, Munira Syed, Oliver Conway, Vishal Juneja, Sriram Subramanian, Nitesh V. Chawla


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Jointly Predicting Job Performance, Personality, Cognitive Ability, Affect, and Well-Being


Jun 10, 2020
Pablo Robles-Granda, Suwen Lin, Xian Wu, Sidney D'Mello, Gonzalo J. Martinez, Koustuv Saha, Kari Nies, Gloria Mark, Andrew T. Campbell, Munmun De Choudhury, Anind D. Dey, Julie Gregg, Ted Grover, Stephen M. Mattingly, Shayan Mirjafari, Edward Moskal, Aaron Striegel, Nitesh V. Chawla


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Few-Shot Knowledge Graph Completion


Nov 26, 2019
Chuxu Zhang, Huaxiu Yao, Chao Huang, Meng Jiang, Zhenhui Li, Nitesh V. Chawla


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Graph Few-shot Learning via Knowledge Transfer


Oct 07, 2019
Huaxiu Yao, Chuxu Zhang, Ying Wei, Meng Jiang, Suhang Wang, Junzhou Huang, Nitesh V. Chawla, Zhenhui Li

* Accepted by NeurIPS 2019 GRL workshop 

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Constructing Information-Lossless Biological Knowledge Graphs from Conditional Statements


Jun 26, 2019
Tianwen Jiang, Tong Zhao, Bing Qin, Ting Liu, Nitesh V. Chawla, Meng Jiang


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Incremental embedding for temporal networks


Apr 06, 2019
Tomasz Kajdanowicz, Kamil Tagowski, Maciej Falkiewicz, Piotr Bielak, Przemysław Kazienko, Nitesh V. Chawla


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A Deep Neural Network for Unsupervised Anomaly Detection and Diagnosis in Multivariate Time Series Data


Nov 20, 2018
Chuxu Zhang, Dongjin Song, Yuncong Chen, Xinyang Feng, Cristian Lumezanu, Wei Cheng, Jingchao Ni, Bo Zong, Haifeng Chen, Nitesh V. Chawla


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Beyond Volume: The Impact of Complex Healthcare Data on the Machine Learning Pipeline


Jan 26, 2018
Keith Feldman, Louis Faust, Xian Wu, Chao Huang, Nitesh V. Chawla

* Towards Integrative Machine Learning and Knowledge Extraction, LNCS vol 10344 (2017) 150-169 
* Healthcare Informatics, Machine Learning, Knowledge Discovery: 20 Pages, 1 Figure 

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Predicting Online Video Engagement Using Clickstreams


May 20, 2014
Everaldo Aguiar, Saurabh Nagrecha, Nitesh V. Chawla


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Predictors of short-term decay of cell phone contacts in a large scale communication network


Feb 11, 2011
Troy Raeder, Omar Lizardo, David Hachen, Nitesh V. Chawla


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