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Journal Articles
- 2022
[j19]Yingxue Zhang
, Yanhua Li, Xun Zhou, Xiangnan Kong, Jun Luo:
Off-Deployment Traffic Estimation - A Traffic Generative Adversarial Networks Approach. IEEE Trans. Big Data 8(4): 1084-1095 (2022)- [j18]Nesreen K. Ahmed, Ryan A. Rossi, John Boaz Lee, Theodore L. Willke, Rong Zhou, Xiangnan Kong, Hoda Eldardiry:
Role-Based Graph Embeddings. IEEE Trans. Knowl. Data Eng. 34(5): 2401-2415 (2022) - [j17]Yao Zhang, Sijia Peng, Yun Xiong, Xiangnan Kong, Xinyue Liu, Yangyong Zhu:
Multi-task attributed graphical lasso and its application in fund classification. World Wide Web 25(3): 1425-1446 (2022) - 2021
- [j16]Yao Zhang, Yun Xiong, Xiangnan Kong, Zhuang Niu, Yangyong Zhu:
IGE+: A Framework for Learning Node Embeddings in Interaction Graphs. IEEE Trans. Knowl. Data Eng. 33(3): 1032-1044 (2021) - [j15]Yun Xiong, Yizhou Zhang, Xiangnan Kong, Huidi Chen, Yangyong Zhu:
GraphInception: Convolutional Neural Networks for Collective Classification in Heterogeneous Information Networks. IEEE Trans. Knowl. Data Eng. 33(5): 1960-1972 (2021) - 2020
- [j14]Yugang Ji, Chuan Shi, Yuan Fang, Xiangnan Kong, Mingyang Yin:
Semi-supervised Co-Clustering on Attributed Heterogeneous Information Networks. Inf. Process. Manag. 57(6): 102338 (2020) - 2019
- [j13]John Boaz Lee, Xiangnan Kong:
Learning compact graph representations via an encoder-decoder network. Appl. Netw. Sci. 4(1): 50:1-50:16 (2019) - [j12]Yafang Li, Caiyan Jia, Xiangnan Kong, Liu Yang, Jian Yu:
Locally Weighted Fusion of Structural and Attribute Information in Graph Clustering. IEEE Trans. Cybern. 49(1): 247-260 (2019) - 2018
- [j11]Yun Xiong, Yizhou Zhang, Xiangnan Kong, Yangyong Zhu:
NetCycle+: A Framework for Collective Evolution Inference in Dynamic Heterogeneous Networks. IEEE Trans. Knowl. Data Eng. 30(8): 1547-1560 (2018) - 2017
- [j10]Maryam Hasan, Elke A. Rundensteiner, Xiangnan Kong, Emmanuel Agu:
Discover trends in public emotion using social sensing. SIGWEB Newsl. 2017(Spring): 2:1-2:5 (2017) - 2015
- [j9]Bokai Cao, Xiangnan Kong, Jingyuan Zhang, Philip S. Yu, Ann B. Ragin:
Identifying HIV-induced subgraph patterns in brain networks with side information. Brain Informatics 2(4): 211-223 (2015) - [j8]Bokai Cao, Xiangnan Kong, Philip S. Yu:
A review of heterogeneous data mining for brain disorder identification. Brain Informatics 2(4): 253-264 (2015) - [j7]Li-Jia Li, David A. Shamma, Xiangnan Kong, Sina Jafarpour, Roelof van Zwol, Xuanhui Wang:
CelebrityNet: A Social Network Constructed from Large-Scale Online Celebrity Images. ACM Trans. Multim. Comput. Commun. Appl. 12(1): 3:1-3:22 (2015) - 2014
- [j6]Chong-Jing Sun, Philip S. Yu, Xiangnan Kong, Yan Fu:
Privacy Preserving Social Network Publication Against Mutual Friend Attacks. Trans. Data Priv. 7(2): 71-97 (2014) - [j5]Chuan Shi, Xiangnan Kong, Di Fu, Philip S. Yu, Bin Wu:
Multi-Label Classification Based on Multi-Objective Optimization. ACM Trans. Intell. Syst. Technol. 5(2): 35:1-35:22 (2014) - [j4]Chuan Shi, Xiangnan Kong, Yue Huang, Philip S. Yu, Bin Wu:
HeteSim: A General Framework for Relevance Measure in Heterogeneous Networks. IEEE Trans. Knowl. Data Eng. 26(10): 2479-2492 (2014) - 2013
- [j3]Xiangnan Kong, Philip S. Yu:
Brain network analysis: a data mining perspective. SIGKDD Explor. 15(2): 30-38 (2013) - [j2]Xiangnan Kong, Michael K. Ng, Zhi-Hua Zhou:
Transductive Multilabel Learning via Label Set Propagation. IEEE Trans. Knowl. Data Eng. 25(3): 704-719 (2013) - 2012
- [j1]Xiangnan Kong, Philip S. Yu:
gMLC: a multi-label feature selection framework for graph classification. Knowl. Inf. Syst. 31(2): 281-305 (2012)
Conference and Workshop Papers
- 2024
- [c109]Jidapa Thadajarassiri, Walter Gerych, Xiangnan Kong, Elke A. Rundensteiner:
Amalgamating Multi-Task Models with Heterogeneous Architectures. AAAI 2024: 15346-15354 - 2023
- [c108]Jidapa Thadajarassiri, Thomas Hartvigsen, Walter Gerych, Xiangnan Kong, Elke A. Rundensteiner:
Knowledge Amalgamation for Multi-Label Classification via Label Dependency Transfer. AAAI 2023: 9980-9988 - [c107]Hang Yin, Yao Su, Xinyue Liu, Thomas Hartvigsen, Yanhua Li, Xiangnan Kong:
Multi-State Brain Network Discovery. IEEE Big Data 2023: 453-462 - [c106]Jianjun Luo, Ryan A. Rossi, Xiangnan Kong, Yanhua Li:
Multi-Item Continuous Influence Maximization. IEEE Big Data 2023: 5282-5291 - [c105]Yao Su, Zhentian Qian, Lei Ma, Lifang He, Xiangnan Kong:
One-shot Joint Extraction, Registration and Segmentation of Neuroimaging Data. KDD 2023: 2049-2060 - 2022
- [c104]Thomas Hartvigsen, Walter Gerych, Jidapa Thadajarassiri, Xiangnan Kong, Elke A. Rundensteiner:
Stop&Hop: Early Classification of Irregular Time Series. CIKM 2022: 696-705 - [c103]Yao Su, Xin Dai, Lifang He, Xiangnan Kong:
ABN: Anti-Blur Neural Networks for Multi-Stage Deformable Image Registration. ICDM 2022: 468-477 - [c102]Yingxue Zhang, Yanhua Li, Xun Zhou, Xiangnan Kong, Jun Luo:
STrans-GAN: Spatially-Transferable Generative Adversarial Networks for Urban Traffic Estimation. ICDM 2022: 743-752 - [c101]Yao Su, Zhentian Qian, Lifang He, Xiangnan Kong:
ERNet: Unsupervised Collective Extraction and Registration in Neuroimaging Data. KDD 2022: 1666-1675 - 2021
- [c100]Jidapa Thadajarassiri, Thomas Hartvigsen, Xiangnan Kong, Elke A. Rundensteiner:
Semi-Supervised Knowledge Amalgamation for Sequence Classification. AAAI 2021: 9859-9867 - [c99]Dongyu Zhang, Cansu Sen, Jidapa Thadajarassiri, Thomas Hartvigsen, Xiangnan Kong, Elke A. Rundensteiner:
Human-like Explanation for Text Classification With Limited Attention Supervision. IEEE BigData 2021: 957-967 - [c98]Xin Dai, Xiangnan Kong, Tian Guo, Xinlu He:
FiShNet: Fine-Grained Filter Sharing for Resource-Efficient Multi-Task Learning. CIKM 2021: 322-331 - [c97]Chao Chen, Yifan Shen, Guixiang Ma, Xiangnan Kong, Srinivas Rangarajan, Xi Zhang, Sihong Xie:
Self-learn to Explain Siamese Networks Robustly. ICDM 2021: 1018-1023 - [c96]Hang Yin, John Boaz Lee, Xiangnan Kong, Thomas Hartvigsen, Sihong Xie:
Energy-Efficient Models for High-Dimensional Spike Train Classification using Sparse Spiking Neural Networks. KDD 2021: 2017-2025 - [c95]Xin Dai, Xiangnan Kong, Tian Guo, Yixian Huang:
CiNet: Redesigning Deep Neural Networks for Efficient Mobile-Cloud Collaborative Inference. SDM 2021: 459-467 - [c94]Samuel S. Ogden, Xiangnan Kong, Tian Guo:
PieSlicer: Dynamically Improving Response Time for Cloud-based CNN Inference. ICPE 2021: 249-256 - 2020
- [c93]Cansu Sen, Thomas Hartvigsen, Biao Yin, Xiangnan Kong, Elke A. Rundensteiner:
Human Attention Maps for Text Classification: Do Humans and Neural Networks Focus on the Same Words? ACL 2020: 4596-4608 - [c92]Yao Zhang, Yun Xiong, Xiangnan Kong, Xinyue Liu, Yangyong Zhu:
Multi-task Attributed Graphical Lasso. APWeb/WAIM (1) 2020: 670-684 - [c91]Xiao Qin, Cao Xiao, Tengfei Ma, Tabassum Kakar, Susmitha Wunnava, Xiangnan Kong, Elke A. Rundensteiner, Fei Wang:
Supervised Topic Compositional Neural Language Model for Clinical Narrative Understanding. IEEE BigData 2020: 758-767 - [c90]Jidapa Thadajarassiri, Cansu Sen, Thomas Hartvigsen, Xiangnan Kong, Elke A. Rundensteiner:
Learning Similarity-Preserving Meta-Embedding for Text Mining. IEEE BigData 2020: 808-817 - [c89]Hang Yin, Xinyue Liu, Xiangnan Kong:
Gaussian Mixture Graphical Lasso with Application to Edge Detection in Brain Networks. IEEE BigData 2020: 1430-1435 - [c88]Zhongfang Zhuang, Xiangnan Kong, Elke A. Rundensteiner, Jihane Zouaoui, Aditya Arora:
MLAS: Metric Learning on Attributed Sequences. IEEE BigData 2020: 1449-1454 - [c87]Xin Dai, Xiangnan Kong, Tian Guo:
EPNet: Learning to Exit with Flexible Multi-Branch Network. CIKM 2020: 235-244 - [c86]Thomas Hartvigsen, Cansu Sen, Xiangnan Kong, Elke A. Rundensteiner:
Learning to Selectively Update State Neurons in Recurrent Networks. CIKM 2020: 485-494 - [c85]Yun Xiong, Shaofeng Xu, Keyao Rong, Xinyue Liu, Xiangnan Kong, Shanshan Li, Philip S. Yu, Yangyong Zhu:
Code2Text: Dual Attention Syntax Annotation Networks for Structure-Aware Code Translation. DASFAA (3) 2020: 87-103 - [c84]Susmitha Wunnava, Xiao Qin, Tabassum Kakar, Xiangnan Kong, Elke A. Rundensteiner:
A Dual-Attention Network for Joint Named Entity Recognition and Sentence Classification of Adverse Drug Events. EMNLP (Findings) 2020: 3414-3423 - [c83]Xin Dai, Xiangnan Kong, Tian Guo, John Boaz Lee, Xinyue Liu, Constance M. Moore:
Recurrent Networks for Guided Multi-Attention Classification. KDD 2020: 412-420 - [c82]Yingxue Zhang, Yanhua Li, Xun Zhou, Xiangnan Kong, Jun Luo:
Curb-GAN: Conditional Urban Traffic Estimation through Spatio-Temporal Generative Adversarial Networks. KDD 2020: 842-852 - [c81]Thomas Hartvigsen, Cansu Sen, Xiangnan Kong, Elke A. Rundensteiner:
Recurrent Halting Chain for Early Multi-label Classification. KDD 2020: 1382-1392 - [c80]Xin Dai, Xiangnan Kong, Xinyue Liu, John Boaz Lee, Constance M. Moore:
Dual-Attention Recurrent Networks for Affine Registration of Neuroimaging Data. SDM 2020: 379-387 - [c79]John Boaz Lee, Xiangnan Kong, Constance M. Moore, Nesreen K. Ahmed:
Deep Parametric Model for Discovering Group-cohesive Functional Brain Regions. SDM 2020: 631-639 - 2019
- [c78]Jianjun Luo, Xinyue Liu, Xiangnan Kong:
Competitive opinion maximization in social networks. ASONAM 2019: 250-257 - [c77]Jidapa Thadajarassiri, Cansu Sen, Thomas Hartvigsen, Xiangnan Kong, Elke A. Rundensteiner:
Comparing General and Locally-Learned Word Embeddings for Clinical Text Mining. BHI 2019: 1-4 - [c76]Cansu Sen, Thomas Hartvigsen, Xiangnan Kong, Elke A. Rundensteiner:
Patient-level Classification on Clinical Note Sequences Guided by Attributed Hierarchical Attention. IEEE BigData 2019: 930-939 - [c75]Zhongfang Zhuang, Xiangnan Kong, Elke A. Rundensteiner, Jihane Zouaoui, Aditya Arora:
Attributed Sequence Embedding. IEEE BigData 2019: 1723-1728 - [c74]Cansu Sen, Thomas Hartvigsen, Xiangnan Kong, Elke A. Rundensteiner:
Learning Temporal Relevance in Longitudinal Medical Notes. IEEE BigData 2019: 2474-2483 - [c73]John Boaz Lee, Ryan A. Rossi, Xiangnan Kong, Sungchul Kim, Eunyee Koh, Anup Rao:
Graph Convolutional Networks with Motif-based Attention. CIKM 2019: 499-508 - [c72]Yuyan Zheng, Chuan Shi, Xiangnan Kong, Yanfang Ye:
Author Set Identification via Quasi-Clique Discovery. CIKM 2019: 771-780 - [c71]Shaofeng Xu, Yun Xiong, Xiangnan Kong, Yangyong Zhu:
Net2Text: An Edge Labelling Language Model for Personalized Review Generation. DASFAA (1) 2019: 484-500 - [c70]Yingxue Zhang, Yanhua Li, Xun Zhou, Xiangnan Kong, Jun Luo:
TrafficGAN: Off-Deployment Traffic Estimation with Traffic Generative Adversarial Networks. ICDM 2019: 1474-1479 - [c69]Thomas Hartvigsen, Cansu Sen, Xiangnan Kong, Elke A. Rundensteiner:
Adaptive-Halting Policy Network for Early Classification. KDD 2019: 101-110 - [c68]Zhongfang Zhuang, Xiangnan Kong, Elke A. Rundensteiner:
AMAS: Attention Model for Attributed Sequence Classification. SDM 2019: 109-117 - [c67]Yao Zhang, Yun Xiong, Lu Ruan, Xiangnan Kong, Yangyong Zhu:
NetMerger: Predicting Cross-network Links in Merged Heterogeneous Networks. WI (Companion) 2019: 21-28 - [c66]Thanh Tran, Xinyue Liu, Kyumin Lee, Xiangnan Kong:
Signed Distance-based Deep Memory Recommender. WWW 2019: 1841-1852 - 2018
- [c65]Yafang Li, Xiangnan Kong, Caiyan Jia, Jianqiang Li:
Clustering Uncertain Graphs with Node Attributes. ACML 2018: 232-247 - [c64]Susmitha Wunnava, Xiao Qin, Tabassum Kakar, Elke A. Rundensteiner, Xiangnan Kong:
Deep Learning Strategies for Automatic Detection of Medication and Adverse Drug Events from Electronic Health Records. AMIA 2018 - [c63]Yun Xiong, Lu Ruan, Mengjie Guo, Chunlei Tang, Xiangnan Kong, Yangyong Zhu, Wei Wang:
Predicting Disease-related Associations by Heterogeneous Network Embedding. BIBM 2018: 548-555 - [c62]Zhongfang Zhuang, Xiangnan Kong, Elke A. Rundensteiner, Aditya Arora, Jihane Zouaoui:
One-Shot Learning on Attributed Sequences. IEEE BigData 2018: 921-930 - [c61]Thomas Hartvigsen, Cansu Sen, Sarah Brownell, Erin Teeple, Xiangnan Kong, Elke A. Rundensteiner:
Early Prediction of MRSA Infections using Electronic Health Records. HEALTHINF 2018: 156-167 - [c60]Susmitha Wunnava, Xiao Qin, Tabassum Kakar, Xiangnan Kong, Elke A. Rundensteiner, Sanjay K. Sahoo, Suranjan De:
One Size Does Not Fit All: An Ensemble Approach Towards Information Extraction from Adverse Drug Event Narratives. HEALTHINF 2018: 176-188 - [c59]Susmitha Wunnava, Xiao Qin, Tabassum Kakar, M. L. Tlachac, Xiangnan Kong, Elke A. Rundensteiner, Sanjay K. Sahoo, Suranjan De:
Multi-layered Learning for Information Extraction from Adverse Drug Event Narratives. BIOSTEC (Selected Papers) 2018: 420-446 - [c58]Chang Liao, Yun Xiong, Xiangnan Kong, Yangyong Zhu:
Tracking Dynamic Magnet Communities: Insights from a Network Perspective. DASFAA (1) 2018: 406-424 - [c57]Chang Liao, Yun Xiong, Xiangnan Kong, Yangyong Zhu, Shimin Zhao, Shanshan Li:
Functional-Oriented Relationship Strength Estimation: From Online Events to Offline Interactions. DASFAA (1) 2018: 442-459 - [c56]Dongqing Xiao, Mohamed Y. Eltabakh, Xiangnan Kong:
Sharing Uncertain Graphs Using Syntactic Private Graph Models. ICDE 2018: 1336-1339 - [c55]Xinyue Liu, Xiangnan Kong, Lei Liu, Kuorong Chiang:
TreeGAN: Syntax-Aware Sequence Generation with Generative Adversarial Networks. ICDM 2018: 1140-1145 - [c54]Hang Yin, Xiangnan Kong, Xinyue Liu:
Coherent Graphical Lasso for Brain Network Discovery. ICDM 2018: 1392-1397 - [c53]John Boaz Lee, Ryan A. Rossi, Xiangnan Kong:
Graph Classification using Structural Attention. KDD 2018: 1666-1674 - [c52]Xinyue Liu, Xiangnan Kong, Philip S. Yu:
Active Opinion Maximization in Social Networks. KDD 2018: 1840-1849 - [c51]Susmitha Wunnava, Xiao Qin, Tabassum Kakar, Elke A. Rundensteiner, Xiangnan Kong:
Bidirectional LSTM-CRF for Adverse Drug Event Tagging in Electronic Health Records. Medication and Adverse Drug Event Detection 2018: 48-56 - [c50]Yizhou Zhang, Yun Xiong, Xiangnan Kong, Shanshan Li, Jinhong Mi, Yangyong Zhu:
Deep Collective Classification in Heterogeneous Information Networks. WWW 2018: 399-408 - 2017
- [c49]Yao Zhang, Yun Xiong, Xiangnan Kong, Yangyong Zhu:
Learning Node Embeddings in Interaction Graphs. CIKM 2017: 397-406 - [c48]Xinyue Liu, Yuanfang Song, Charu C. Aggarwal, Yao Zhang, Xiangnan Kong:
BiCycle: Item Recommendation with Life Cycles. ICDM 2017: 297-306 - [c47]Saket Sathe, Charu C. Aggarwal, Xiangnan Kong, Xinyue Liu:
Kernel-Based Feature Extraction for Collaborative Filtering. ICDM 2017: 1057-1062 - [c46]Xinyue Liu, Xiangnan Kong, Philip S. Yu:
Collective discovery of brain networks with unknown groups. IJCNN 2017: 3569-3576 - [c45]Xinyue Liu, Xiangnan Kong, Ann B. Ragin:
Unified and Contrasting Graphical Lasso for Brain Network Discovery. SDM 2017: 180-188 - [c44]John Boaz Lee, Xiangnan Kong, Yihan Bao, Constance M. Moore:
Identifying Deep Contrasting Networks from Time Series Data: Application to Brain Network Analysis. SDM 2017: 543-551 - [c43]Yao Zhang, Yun Xiong, Xinyue Liu, Xiangnan Kong, Yangyong Zhu:
Meta-Path Graphical Lasso for Learning Heterogeneous Connectivities. SDM 2017: 642-650 - [c42]Maryam Hasan, Elke A. Rundensteiner, Xiangnan Kong, Emmanuel Agu:
Using Social Sensing to Discover Trends in Public Emotion. ICSC 2017: 172-179 - 2016
- [c41]Jiawei Zhang, Xiangnan Kong, Philip S. Yu:
Social badge system analysis. ASONAM 2016: 453-460 - [c40]Xinyue Liu, Xiangnan Kong, Yanhua Li:
Collective Traffic Prediction with Partially Observed Traffic History using Location-Based Social Media. CIKM 2016: 2179-2184 - [c39]Yizhou Zhang, Yun Xiong, Xiangnan Kong, Yangyong Zhu:
NetCycle: Collective Evolution Inference in Heterogeneous Information Networks. KDD 2016: 1365-1374 - [c38]Xinyue Liu, Charu C. Aggarwal, Yufeng Li, Xiangnan Kong, Xinyuan Sun, Saket Sathe:
Kernelized Matrix Factorization for Collaborative Filtering. SDM 2016: 378-386 - [c37]Dongqing Xiao, Mohamed Y. Eltabakh, Xiangnan Kong:
Bermuda: An Efficient MapReduce Triangle Listing Algorithm for Web-Scale Graphs. SSDBM 2016: 10:1-10:12 - 2015
- [c36]Bokai Cao, Liang Zhan, Xiangnan Kong, Philip S. Yu, Nathalie Vizueta, Lori L. Altshuler, Alex D. Leow:
Identification of Discriminative Subgraph Patterns in fMRI Brain Networks in Bipolar Affective Disorder. BIH 2015: 105-114 - [c35]Bokai Cao, Xiangnan Kong, Jingyuan Zhang, Philip S. Yu, Ann B. Ragin:
Mining Brain Networks Using Multiple Side Views for Neurological Disorder Identification. ICDM 2015: 709-714 - [c34]Jiawei Zhang, Weixiang Shao, Senzhang Wang, Xiangnan Kong, Philip S. Yu:
PNA: Partial Network Alignment with Generic Stable Matching. IRI 2015: 166-173 - [c33]Randy C. Paffenroth, Xiangnan Kong:
Python in Data Science Research and Education. SciPy 2015: 164-170 - 2014
- [c32]Jingyuan Zhang, Xiangnan Kong, Roger Jie Luo, Yi Chang, Philip S. Yu:
NCR: A Scalable Network-Based Approach to Co-Ranking in Question-and-Answer Sites. CIKM 2014: 709-718 - [c31]Bokai Cao, Lifang He, Xiangnan Kong, Philip S. Yu, Zhifeng Hao, Ann B. Ragin:
Tensor-Based Multi-view Feature Selection with Applications to Brain Diseases. ICDM 2014: 40-49 - [c30]Bokai Cao, Xiangnan Kong, Philip S. Yu:
Collective Prediction of Multiple Types of Links in Heterogeneous Information Networks. ICDM 2014: 50-59 - [c29]Jingyuan Zhang, Xiaoxiao Shi, Xiangnan Kong, Hong-Han Shuai, Philip S. Yu:
Discovering Organizational Correlations from Twitter. ICDM Workshops 2014: 243-250 - [c28]Lifang He, Hong-Han Shuai, Xiangnan Kong, Zhifeng Hao, Xiaowei Yang, Philip S. Yu:
Low-Density Cut Based Tree Decomposition for Large-Scale SVM Problems. ICDM 2014: 839-844 - [c27]Li-Jia Li, Xiangnan Kong, Philip S. Yu:
Visual Recognition by Exploiting Latent Social Links in Image Collections. MMM (1) 2014: 121-132 - [c26]Lifang He, Xiangnan Kong, Philip S. Yu, Xiaowei Yang, Ann B. Ragin, Zhifeng Hao:
DuSK: A Dual Structure-preserving Kernel for Supervised Tensor Learning with Applications to Neuroimages. SDM 2014: 127-135 - [c25]Ning Yang, Xiangnan Kong, Fengjiao Wang, Philip S. Yu:
When and Where: Predicting Human Movements Based on Social Spatial-Temporal Events. SDM 2014: 515-523 - [c24]Xiangnan Kong, Zhaoming Wu, Li-Jia Li, Ruofei Zhang, Philip S. Yu, Hang Wu, Wei Fan:
Large-Scale Multi-Label Learning with Incomplete Label Assignments. SDM 2014: 920-928 - [c23]Jiawei Zhang, Xiangnan Kong, Philip S. Yu:
Transferring heterogeneous links across location-based social networks. WSDM 2014: 303-312 - [c22]Chun-Ta Lu, Sihong Xie, Xiangnan Kong, Philip S. Yu:
Inferring the impacts of social media on crowdfunding. WSDM 2014: 573-582 - 2013
- [c21]Xiangnan Kong, Jiawei Zhang, Philip S. Yu:
Inferring anchor links across multiple heterogeneous social networks. CIKM 2013: 179-188 - [c20]Shuyang Lin, Xiangnan Kong, Philip S. Yu:
Predicting trends in social networks via dynamic activeness model. CIKM 2013: 1661-1666 - [c19]Chong-Jing Sun, Philip S. Yu, Xiangnan Kong, Yan Fu:
Privacy Preserving Social Network Publication against Mutual Friend Attacks. ICDM Workshops 2013: 883-890 - [c18]Sihong Xie, Xiangnan Kong, Jing Gao, Wei Fan, Philip S. Yu:
Multilabel Consensus Classification. ICDM 2013: 1241-1246 - [c17]Jiawei Zhang, Xiangnan Kong, Philip S. Yu:
Predicting Social Links for New Users across Aligned Heterogeneous Social Networks. ICDM 2013: 1289-1294 - [c16]Xiangnan Kong, Bokai Cao, Philip S. Yu:
Multi-label classification by mining label and instance correlations from heterogeneous information networks. KDD 2013: 614-622 - [c15]Xiangnan Kong, Ann B. Ragin, Xue Wang, Philip S. Yu:
Discriminative Feature Selection for Uncertain Graph Classification. SDM 2013: 82-93 - 2012
- [c14]Erjia Yan, Ying Ding, Xiangnan Kong:
Monitoring knowledge flow through scholarly networks. ASIST 2012: 1-5 - [c13]Xiangnan Kong, Philip S. Yu, Ying Ding, David J. Wild:
Meta path-based collective classification in heterogeneous information networks. CIKM 2012: 1567-1571 - [c12]Chuan Shi, Xiangnan Kong, Philip S. Yu, Sihong Xie, Bin Wu:
Relevance search in heterogeneous networks. EDBT 2012: 180-191 - [c11]Chuan Shi, Chong Zhou, Xiangnan Kong, Philip S. Yu, Gang Liu, Bai Wang:
HeteRecom: a semantic-based recommendation systemin heterogeneous networks. KDD 2012: 1552-1555 - [c10]Chuan Shi, Xiangnan Kong, Philip S. Yu, Bai Wang:
Multi-Objective Multi-Label Classification. SDM 2012: 355-366 - [c9]Xiaoxiao Shi, Xiangnan Kong, Philip S. Yu:
Transfer Significant Subgraphs across Graph Databases. SDM 2012: 552-563 - [c8]Wangqun Lin, Xiangnan Kong, Philip S. Yu, Quanyuan Wu, Yan Jia, Chuan Li:
Community detection in incomplete information networks. WWW 2012: 341-350 - 2011
- [c7]Xiangnan Kong, Philip S. Yu:
An ensemble-based approach to fast classification of multi-label data streams. CollaborateCom 2011: 95-104 - [c6]Yuchen Zhao, Xiangnan Kong, Philip S. Yu:
Positive and Unlabeled Learning for Graph Classification. ICDM 2011: 962-971 - [c5]Xiangnan Kong, Wei Fan, Philip S. Yu:
Dual active feature and sample selection for graph classification. KDD 2011: 654-662 - [c4]Chuan Shi, Xiangnan Kong, Philip S. Yu, Bai Wang:
Multi-label Ensemble Learning. ECML/PKDD (3) 2011: 223-239 - [c3]Xiangnan Kong, Xiaoxiao Shi, Philip S. Yu:
Multi-Label Collective Classification. SDM 2011: 618-629 - 2010
- [c2]Xiangnan Kong, Philip S. Yu:
Multi-label Feature Selection for Graph Classification. ICDM 2010: 274-283 - [c1]Xiangnan Kong, Philip S. Yu:
Semi-supervised feature selection for graph classification. KDD 2010: 793-802
Parts in Books or Collections
- 2014
- [p1]Charu C. Aggarwal, Xiangnan Kong, Quanquan Gu, Jiawei Han, Philip S. Yu:
Active Learning: A Survey. Data Classification: Algorithms and Applications 2014: 571-606
Reference Works
- 2018
- [r1]Xiangnan Kong, Philip S. Yu:
Graph Classification in Heterogeneous Networks. Encyclopedia of Social Network Analysis and Mining. 2nd Ed. 2018
Informal and Other Publications
- 2023
- [i33]Thomas Hartvigsen, Jidapa Thadajarassiri, Xiangnan Kong, Elke A. Rundensteiner:
Finding Short Signals in Long Irregular Time Series with Continuous-Time Attention Policy Networks. CoRR abs/2302.04052 (2023) - [i32]Yao Su, Zhentian Qian, Lei Ma, Lifang He, Xiangnan Kong:
One-shot Joint Extraction, Registration and Segmentation of Neuroimaging Data. CoRR abs/2307.15198 (2023) - [i31]Hang Yin, Yao Su, Xinyue Liu, Thomas Hartvigsen, Yanhua Li, Xiangnan Kong:
Multi-State Brain Network Discovery. CoRR abs/2311.02466 (2023) - 2022
- [i30]Zhongfang Zhuang, Xiangnan Kong, Elke A. Rundensteiner, Aditya Arora, Jihane Zouaoui:
One-Shot Learning on Attributed Sequences. CoRR abs/2201.09202 (2022) - [i29]Thomas Hartvigsen, Walter Gerych, Jidapa Thadajarassiri, Xiangnan Kong, Elke A. Rundensteiner:
Stop&Hop: Early Classification of Irregular Time Series. CoRR abs/2208.09795 (2022) - [i28]Yao Su, Xin Dai, Lifang He, Xiangnan Kong:
ABN: Anti-Blur Neural Networks for Multi-Stage Deformable Image Registration. CoRR abs/2212.03277 (2022) - [i27]Yao Su, Zhentian Qian, Lifang He, Xiangnan Kong:
ERNet: Unsupervised Collective Extraction and Registration in Neuroimaging Data. CoRR abs/2212.03306 (2022) - 2021
- [i26]Hang Yin, Xinyue Liu, Xiangnan Kong:
Gaussian Mixture Graphical Lasso with Application to Edge Detection in Brain Networks. CoRR abs/2101.05348 (2021) - [i25]Chao Chen, Yifan Shen, Guixiang Ma, Xiangnan Kong, Srinivas Rangarajan, Xi Zhang, Sihong Xie:
Self-learn to Explain Siamese Networks Robustly. CoRR abs/2109.07371 (2021) - 2020
- [i24]Zhongfang Zhuang, Xiangnan Kong, Elke A. Rundensteiner, Jihane Zouaoui, Aditya Arora:
MLAS: Metric Learning on Attributed Sequences. CoRR abs/2011.04062 (2020) - 2019
- [i23]Thanh Tran, Xinyue Liu, Kyumin Lee, Xiangnan Kong:
Signed Distance-based Deep Memory Recommender. CoRR abs/1905.00453 (2019) - [i22]Zhongfang Zhuang, Xiangnan Kong, Elke A. Rundensteiner, Jihane Zouaoui, Aditya Arora:
Attributed Sequence Embedding. CoRR abs/1911.00949 (2019) - 2018
- [i21]Nesreen K. Ahmed, Ryan A. Rossi, John Boaz Lee, Xiangnan Kong, Theodore L. Willke, Rong Zhou, Hoda Eldardiry:
Learning Role-based Graph Embeddings. CoRR abs/1802.02896 (2018) - [i20]Xinyue Liu, Xiangnan Kong, Lei Liu, Kuorong Chiang:
TreeGAN: Syntax-Aware Sequence Generation with Generative Adversarial Networks. CoRR abs/1808.07582 (2018) - [i19]John Boaz Lee, Ryan A. Rossi, Xiangnan Kong, Sungchul Kim, Eunyee Koh, Anup Rao:
Higher-order Graph Convolutional Networks. CoRR abs/1809.07697 (2018) - 2017
- [i18]Nesreen K. Ahmed, Ryan A. Rossi, Rong Zhou, John Boaz Lee, Xiangnan Kong, Theodore L. Willke, Hoda Eldardiry:
A Framework for Generalizing Graph-based Representation Learning Methods. CoRR abs/1709.04596 (2017) - [i17]John Boaz Lee, Ryan A. Rossi, Xiangnan Kong:
Deep Graph Attention Model. CoRR abs/1709.06075 (2017) - [i16]Nesreen K. Ahmed, Ryan A. Rossi, Rong Zhou, John Boaz Lee, Xiangnan Kong, Theodore L. Willke, Hoda Eldardiry:
Representation Learning in Large Attributed Graphs. CoRR abs/1710.09471 (2017) - 2016
- [i15]Jiawei Zhang, Xiangnan Kong, Philip S. Yu:
Badge System Analysis and Design. CoRR abs/1607.00537 (2016) - 2015
- [i14]Jiawei Zhang, Weixiang Shao, Senzhang Wang, Xiangnan Kong, Philip S. Yu:
Partial Network Alignment with Anchor Meta Path and Truncated Generic Stable Matching. CoRR abs/1506.05164 (2015) - [i13]Bokai Cao, Xiangnan Kong, Philip S. Yu:
A review of heterogeneous data mining for brain disorders. CoRR abs/1508.01023 (2015) - [i12]Bokai Cao, Xiangnan Kong, Jingyuan Zhang, Philip S. Yu, Ann B. Ragin:
Mining Brain Networks using Multiple Side Views for Neurological Disorder Identification. CoRR abs/1508.04554 (2015) - 2014
- [i11]Chong-Jing Sun, Philip S. Yu, Xiangnan Kong, Yan Fu:
Privacy Preserving Social Network Publication Against Mutual Friend Attacks. CoRR abs/1401.3201 (2014) - [i10]Ning Yang, Xiangnan Kong, Fengjiao Wang, Philip S. Yu:
When and Where: Predicting Human Movements Based on Social Spatial-Temporal Events. CoRR abs/1407.1450 (2014) - [i9]Xiangnan Kong, Zhaoming Wu, Li-Jia Li, Ruofei Zhang, Philip S. Yu, Hang Wu, Wei Fan:
Large-Scale Multi-Label Learning with Incomplete Label Assignments. CoRR abs/1407.1538 (2014) - [i8]Lifang He, Xiangnan Kong, Philip S. Yu, Ann B. Ragin, Zhifeng Hao, Xiaowei Yang:
DuSK: A Dual Structure-preserving Kernel for Supervised Tensor Learning with Applications to Neuroimages. CoRR abs/1407.8289 (2014) - [i7]Jingyuan Zhang, Xiaoxiao Shi, Xiangnan Kong, Hong-Han Shuai, Philip S. Yu:
Discovering Organizational Correlations from Twitter. CoRR abs/1410.6001 (2014) - 2013
- [i6]Xiangnan Kong, Philip S. Yu, Xue Wang, Ann B. Ragin:
Discriminative Feature Selection for Uncertain Graph Classification. CoRR abs/1301.6626 (2013) - [i5]Xiangnan Kong, Bokai Cao, Philip S. Yu, Ying Ding, David J. Wild:
Meta Path-Based Collective Classification in Heterogeneous Information Networks. CoRR abs/1305.4433 (2013) - [i4]Shuyang Lin, Xiangnan Kong, Philip S. Yu:
Predicting Trends in Social Networks via Dynamic Activeness Model. CoRR abs/1308.1995 (2013) - [i3]Chuan Shi, Xiangnan Kong, Yue Huang, Philip S. Yu, Bin Wu:
HeteSim: A General Framework for Relevance Measure in Heterogeneous Networks. CoRR abs/1309.7393 (2013) - [i2]Jiawei Zhang, Xiangnan Kong, Philip S. Yu:
Predicting Social Links for New Users across Aligned Heterogeneous Social Networks. CoRR abs/1310.3492 (2013) - [i1]Sihong Xie, Xiangnan Kong, Jing Gao, Wei Fan, Philip S. Yu:
Multilabel Consensus Classification. CoRR abs/1310.4252 (2013)
Coauthor Index
[j9] [j8] [j6] [j5] [j4] [j3] [j1] [c85] [c52] [c46] [c41] [c36] [c35] [c34] [c32] [c31] [c30] [c29] [c28] [c27] [c26] [c25] [c24] [c23] [c22] [c21] [c20] [c19] [c18] [c17] [c16] [c15] [c13] [c12] [c11] [c10] [c9] [c8] [c7] [c6] [c5] [c4] [c3] [c2] [c1] [p1] [r1] [i15] [i14] [i13] [i12] [i11] [i10] [i9] [i8] [i7] [i6] [i5] [i4] [i3] [i2] [i1]
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