Xiangnan Kong

Philip S. Yu, Xiangnan Kong, Xiangnan Kong, 2011, 7th International Conference on Collaborative Computing: Networking, Applications and Worksharing (CollaborateCom).

HeteSim: A General Framework for Relevance Measure in Heterogeneous Networks

Philip S. Yu, Bin Wu, Yue Huang, 2013, IEEE Transactions on Knowledge and Data Engineering.

Relevance search in heterogeneous networks

Philip S. Yu, Bin Wu, Sihong Xie, 2012, EDBT '12.

Discovering Organizational Correlations from Twitter

Philip S. Yu, Hong-Han Shuai, Jingyuan Zhang, 2014, 2014 IEEE International Conference on Data Mining Workshop.

HeteRecom: a semantic-based recommendation system in heterogeneous networks

Philip S. Yu, Gang Liu, Bai Wang, 2012, KDD.

Tensor-Based Multi-view Feature Selection with Applications to Brain Diseases

Philip S. Yu, Lifang He, Xiangnan Kong, 2014, 2014 IEEE International Conference on Data Mining.

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

Philip S. Yu, Xiangnan Kong, Zhifeng Hao, 2014, SDM.

PNA: Partial Network Alignment with Generic Stable Matching

Philip S. Yu, Weixiang Shao, Senzhang Wang, 2015, 2015 IEEE International Conference on Information Reuse and Integration.

Predicting Social Links for New Users across Aligned Heterogeneous Social Networks

Philip S. Yu, Xiangnan Kong, Jiawei Zhang, 2013, 2013 IEEE 13th International Conference on Data Mining.

Positive and Unlabeled Learning for Graph Classification

Philip S. Yu, Yuchen Zhao, Xiangnan Kong, 2011, 2011 IEEE 11th International Conference on Data Mining.

Partial Network Alignment with Anchor Meta Path and Truncated Generic Stable Matching pdf

Philip S. Yu, Weixiang Shao, Senzhang Wang, 2015, ArXiv.

Kernelized Matrix Factorization for Collaborative Filtering

Charu C. Aggarwal, Xiangnan Kong, Xinyue Liu, 2016, SDM.

Off-Deployment Traffic Estimation — A Traffic Generative Adversarial Networks Approach

Xiangnan Kong, Jun Luo, Yingxue Zhang, 2022, IEEE Transactions on Big Data.

Learning compact graph representations via an encoder-decoder network

John Boaz Lee, Xiangnan Kong, Xiangnan Kong, 2019, Applied Network Science.

Deep Graph Attention Model pdf

Ryan A. Rossi, John Boaz Lee, Xiangnan Kong, 2017, ArXiv.

Skip-graph: Learning graph embeddings with an encoder-decoder model

Xiangnan Kong, John Boaz Lee, Xiangnan Kong, 2016 .

Under Consideration for Publication in Knowledge and Information Systems Gmlc: a Multi-label Feature Selection Framework for Graph Classification

Philip S. Yu, Xiangnan Kong, Xiangnan Kong, 2011 .

Dual active feature and sample selection for graph classification

Philip S. Yu, Wei Fan, Xiangnan Kong, 2011, KDD.

Role-Based Graph Embeddings

Ryan A. Rossi, Nesreen K. Ahmed, Rong Zhou, 2020, IEEE Transactions on Knowledge and Data Engineering.

MLAS: Metric Learning on Attributed Sequences pdf

Elke A. Rundensteiner, Xiangnan Kong, Zhongfang Zhuang, 2020, 2020 IEEE International Conference on Big Data (Big Data).

Adaptive-Halting Policy Network for Early Classification

Elke A. Rundensteiner, Xiangnan Kong, Thomas Hartvigsen, 2019, KDD.

Dual-Attention Recurrent Networks for Affine Registration of Neuroimaging Data

John Boaz Lee, Xiangnan Kong, Xinyue Liu, 2020, SDM.

Bermuda: An Efficient MapReduce Triangle Listing Algorithm for Web-Scale Graphs

Xiangnan Kong, Mohamed Y. Eltabakh, Dongqing Xiao, 2016, SSDBM.

Inductive Representation Learning in Large Attributed Graphs

Ryan A. Rossi, Rong Zhou, Nesreen K. Ahmed, 2017 .

Multi-Label Classification Based on Multi-Objective Optimization

Philip S. Yu, Bin Wu, Xiangnan Kong, 2014, TIST.

Multi-Objective Multi-Label Classification

Philip S. Yu, Bai Wang, Xiangnan Kong, 2012, SDM.

gMLC: a multi-label feature selection framework for graph classification

Philip S. Yu, Xiangnan Kong, 2011, Knowledge and Information Systems.

Multilabel Consensus Classification

Philip S. Yu, Wei Fan, Sihong Xie, 2013, 2013 IEEE 13th International Conference on Data Mining.

One-shot Joint Extraction, Registration and Segmentation of Neuroimaging Data

Xiangnan Kong, Lifang He, Zhentian Qian, 2023, KDD.

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

Hang Yin, Xiangnan Kong, Xinyue Liu, 2020, 2020 IEEE International Conference on Big Data (Big Data).

Recurrent Networks for Guided Multi-Attention Classification

John Boaz Lee, Xiangnan Kong, Xinyue Liu, 2020, KDD.

Coherent Graphical Lasso for Brain Network Discovery

Hang Yin, Xiangnan Kong, Xinyue Liu, 2018, 2018 IEEE International Conference on Data Mining (ICDM).

Collective discovery of brain networks with unknown groups

Philip S. Yu, Xiangnan Kong, Xinyue Liu, 2017, 2017 International Joint Conference on Neural Networks (IJCNN).

Deep Parametric Model for Discovering Group-cohesive Functional Brain Regions

Nesreen K. Ahmed, John Boaz Lee, Xiangnan Kong, 2020, SDM.

Identifying Deep Contrasting Networks from Time Series Data: Application to Brain Network Analysis

John Boaz Lee, Xiangnan Kong, Constance M. Moore, 2017, SDM.

Unified and Contrasting Graphical Lasso for Brain Network Discovery

Xiangnan Kong, Xinyue Liu, Ann B. Ragin, 2017, SDM.

Multi-layered Learning for Information Extraction from Adverse Drug Event Narratives

Elke A. Rundensteiner, Tabassum Kakar, Xiao Qin, 2018, BIOSTEC.

One Size Does Not Fit All: An Ensemble Approach Towards Information Extraction from Adverse Drug Event Narratives

Elke A. Rundensteiner, Tabassum Kakar, Sanjay K. Sahoo, 2018, HEALTHINF.

Semi-supervised Co-Clustering on Attributed Heterogeneous Information Networks

Xiangnan Kong, Yugang Ji, Yuan Fang, 2020, Inf. Process. Manag..

Finding Short Signals in Long Irregular Time Series with Continuous-Time Attention Policy Networks

E. Rundensteiner, Xiangnan Kong, Jidapa Thadajarassiri, 2023, ArXiv.

Stop&Hop: Early Classification of Irregular Time Series

E. Rundensteiner, Xiangnan Kong, Jidapa Thadajarassiri, 2022, CIKM.

IGE+: A Framework for Learning Node Embeddings in Interaction Graphs

Yangyong Zhu, Xiangnan Kong, Yun Xiong, 2019, IEEE Transactions on Knowledge and Data Engineering.

Learning Node Embeddings in Interaction Graphs

Yao Zhang, Yangyong Zhu, Xiangnan Kong, 2017, CIKM.

Python in Data Science Research and Education

Randy C. Paffenroth, Xiangnan Kong, R. Paffenroth, 2015, SciPy.

Multi-task attributed graphical lasso and its application in fund classification

Xiangnan Kong, Xinyue Liu, Yangyong Zhu, 2021, World Wide Web.

TreeGAN: Syntax-Aware Sequence Generation with Generative Adversarial Networks

Lei Liu, Xiangnan Kong, Xinyue Liu, 2018, 2018 IEEE International Conference on Data Mining (ICDM).

Active Learning: A Survey

Philip S. Yu, Charu C. Aggarwal, Jiawei Han, 2014, Data Classification: Algorithms and Applications.

Community detection in incomplete information networks

Philip S. Yu, Chuan Li, Quanyuan Wu, 2012, WWW.

Predicting Disease-related Associations by Heterogeneous Network Embedding

Wei Wang, Yangyong Zhu, Xiangnan Kong, 2018, 2018 IEEE International Conference on Bioinformatics and Biomedicine (BIBM).

Active Opinion Maximization in Social Networks

Philip S. Yu, Xiangnan Kong, Xinyue Liu, 2018, KDD.

Identifying HIV-induced subgraph patterns in brain networks with side information

Philip S. Yu, Xiangnan Kong, Bokai Cao, 2015, Brain Informatics.

ERNet: Unsupervised Collective Extraction and Registration in Neuroimaging Data

Xiangnan Kong, Lifang He, Zhentian Qian, 2022, Knowledge Discovery and Data Mining.

Discriminative Feature Selection for Uncertain Graph Classification

Philip S. Yu, Xiangnan Kong, Ann B. Ragin, 2013, SDM.

Meta-Path Graphical Lasso for Learning Heterogeneous Connectivities

Yao Zhang, Yangyong Zhu, Xiangnan Kong, 2017, SDM.