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Gaussian Mixture Graphical Lasso with Application to Edge Detection in Brain Networks


Jan 13, 2021
Hang Yin, Xinyue Liu, Xiangnan Kong


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MLAS: Metric Learning on Attributed Sequences


Nov 08, 2020
Zhongfang Zhuang, Xiangnan Kong, Elke Rundensteiner, Jihane Zouaoui, Aditya Arora

* Accepted by IEEE Big Data 2020 

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Attributed Sequence Embedding


Nov 03, 2019
Zhongfang Zhuang, Xiangnan Kong, Elke Rundensteiner, Jihane Zouaoui, Aditya Arora

* Accepted by IEEE Big Data 2019 

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Signed Distance-based Deep Memory Recommender


May 01, 2019
Thanh Tran, Xinyue Liu, Kyumin Lee, Xiangnan Kong

* Proceedings of the 2019 World Wide Web Conference 

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Higher-order Graph Convolutional Networks


Sep 12, 2018
John Boaz Lee, Ryan A. Rossi, Xiangnan Kong, Sungchul Kim, Eunyee Koh, Anup Rao


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TreeGAN: Syntax-Aware Sequence Generation with Generative Adversarial Networks


Aug 22, 2018
Xinyue Liu, Xiangnan Kong, Lei Liu, Kuorong Chiang

* IEEE International Conference on Data Mining (ICDM'18) 

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Learning Role-based Graph Embeddings


Jul 02, 2018
Nesreen K. Ahmed, Ryan Rossi, John Boaz Lee, Theodore L. Willke, Rong Zhou, Xiangnan Kong, Hoda Eldardiry

* StarAI workshop @ IJCAI 2018 

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Inductive Representation Learning in Large Attributed Graphs


Nov 22, 2017
Nesreen K. Ahmed, Ryan A. Rossi, Rong Zhou, John Boaz Lee, Xiangnan Kong, Theodore L. Willke, Hoda Eldardiry

* NIPS WiML 

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Deep Graph Attention Model


Sep 15, 2017
John Boaz Lee, Ryan Rossi, Xiangnan Kong


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A Framework for Generalizing Graph-based Representation Learning Methods


Sep 14, 2017
Nesreen K. Ahmed, Ryan A. Rossi, Rong Zhou, John Boaz Lee, Xiangnan Kong, Theodore L. Willke, Hoda Eldardiry


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Mining Brain Networks using Multiple Side Views for Neurological Disorder Identification


Aug 19, 2015
Bokai Cao, Xiangnan Kong, Jingyuan Zhang, Philip S. Yu, Ann B. Ragin

* in Proceedings of IEEE International Conference on Data Mining (ICDM) 2015 

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A review of heterogeneous data mining for brain disorders


Aug 05, 2015
Bokai Cao, Xiangnan Kong, Philip S. Yu


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DuSK: A Dual Structure-preserving Kernel for Supervised Tensor Learning with Applications to Neuroimages


Aug 05, 2014
Lifang He, Xiangnan Kong, Philip S. Yu, Ann B. Ragin, Zhifeng Hao, Xiaowei Yang

* 9 pages, 6 figures, conference,Proceedings of the 14th SIAM International Conference on Data Mining (SDM14), Philadelphia, USA, 2014 

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Large-Scale Multi-Label Learning with Incomplete Label Assignments


Jul 06, 2014
Xiangnan Kong, Zhaoming Wu, Li-Jia Li, Ruofei Zhang, Philip S. Yu, Hang Wu, Wei Fan


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Multilabel Consensus Classification


Oct 16, 2013
Sihong Xie, Xiangnan Kong, Jing Gao, Wei Fan, Philip S. Yu


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Predicting Social Links for New Users across Aligned Heterogeneous Social Networks


Oct 13, 2013
Jiawei Zhang, Xiangnan Kong, Philip S. Yu

* 11 pages, 10 figures, 4 tables 

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HeteSim: A General Framework for Relevance Measure in Heterogeneous Networks


Sep 28, 2013
Chuan Shi, Xiangnan Kong, Yue Huang, Philip S. Yu, Bin Wu


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Meta Path-Based Collective Classification in Heterogeneous Information Networks


May 20, 2013
Xiangnan Kong, Bokai Cao, Philip S. Yu, Ying Ding, David J. Wild


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Discriminative Feature Selection for Uncertain Graph Classification


Jan 28, 2013
Xiangnan Kong, Philip S. Yu, Xue Wang, Ann B. Ragin


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