# Xiao Wang（王啸）

Professor

School of Software, Beihang University, Beijing, China

Email: xiao_wang[AT]buaa[DOT]edu[DOT]cn

# Brief Bio

He is a Professor at Beihang University, China. Prior to BUAA, he was an Associate Professor at Beijing University of Posts and Telecommunications. His research interests are graph neural networks, data mining and machine learning. He was a postdoc in the Department of Computer Science and Technology at Tsinghua University. He got the Ph.D. in the School of Computer Science and Technology at Tianjin University. He visited Washington University in St. Louis as a visiting student. He has published 100+ peer reviewed papers in top venues in AI, where 7 papers are selected as the most influential papers, 1 paper got WWW 2021 best paper awards nomination, 1 paper got the ICDM 2021 best student paper runner-up, 4 papers are ESI highly cited, and several works are integrated into the standard graph learning platform PyG and DGL. He obtains the Second Prize of National Natural Science Award, the First Prize of Natural Science Award of the Ministry of Education, the First Prize in Technology Progress Award of CIE, the WuWenJun AI Excellent Young Scientist Award of CAAI, and the ACM China Rising Star Nomination. He is the World's Top 2% Scientists by Stanford University from 2021 to 2023, and the AI2000 Most Influential Scholar Honorable Mention by AMiner in 2022-2024. He also serves as SPC/PC in ACM SIGKDD, AAAI, IJCAI, WWW, etc, and AE for IEEE TAI. He has presided the project of National Nature Science Foundation of China (NSFC) and CCF-Tencent Open Research Fund.

王啸，北京航空航天大学教授，博士生导师。研究方向为图神经网络、数据挖掘与机器学习。共发表论文100余篇，谷歌学术引用10000余次，其中CCF A类论文60余篇，7篇入选最有影响力论文榜单，3次获得(提名)CCF A/B类等国际会议论文奖，ESI高被引论文4篇，成果多次被写入业界图学习标准库PyG和DGL等。出版教材一部，专著四部，著作章节一章。曾获得国家自然科学二等奖，教育部自然科学一等奖，中国电子学会科技进步一等奖，吴文俊人工智能优秀青年奖，ACM中国新星提名奖，2021-2023年入选斯坦福大学发布的全球Top 2%顶尖科学家榜单，2022-2024年入选AMiner评选的AI2000最具影响力学者Honorable mention。担任WWW/AAAI/IJCAI的高级程序委员会委员，IEEE TAI期刊编委。CCF高级会员，CCF大数据专委会执行委员，CCFAI专委会执行委员，中文信息学会SMP专委会委员，CCF青工委主任助理，北京智源研究院青源会会员。 主持国家自然科学优秀青年基金、面上项目等。

I am looking for self-motivated candidates who have solid mathematical backgrounds, strong English ability, and strong coding skills, including senior undergraduate and postgraduate. If you are interested, please drop me an e-mail with your detailed CV.

# News

[10/24] One paper was accepted by IEEE TIP.

[09/24] Invited to serve as Associate Editor for Neural Networks.

[09/24] One paper was accepted by NeurIPS 2024.

[09/24] Top 2% Scientists Worldwide 2024 by Stanford University

[07/24] One paper was accepted by ECAI 2024.

[05/24] One paper was accepted by ICML 2024.

[04/24] One paper was accepted by IEEE TNNLS.

[03/24] AI 2000 Most Influential Scholar Honorable Mention in AAAI/IJCAI, IR and Recommendation

[01/24] Two papers were accepted by WWW 2024.

[12/23] Invited to serve as Associate Editor for IEEE TAI.

[12/23] Two papers were accepted by AAAI 2024.

[11/23] One paper was accepted by ACM TOIS.

[10/23] Top 2% Scientists Worldwide 2023 by Stanford University.

[09/23] One paper was accepted by IEEE TPAMI.

[09/23] Three papers was accepted by NeurIPS 2023.

[06/23] One paper was accepted by ECML-PKDD 2023.

# Publications [Google Scholar (opens new window)]

  • 2024

[C1] Yibo Li, Xiao Wang, Hongrui Liu, Chuan Shi. A Generalized Neural Diffusion Framework on Graphs. AAAI 2024. (CCF-A)

[C2] Yanhu Mo, Xiao Wang*, Shaohua Fan, Chuan Shi. Graph Contrastive Invariant Learning from the Causal Perspective. AAAI 2024. (CCF-A)

[C3] Yibo Li, Xiao Wang*, Yujie Xing, Shaohua Fan, Ruijia Wang, Yaoqi Liu, Chuan Shi. Graph Fairness Learning under Distribution Shifts. WWW 2024 (CCF-A)

[C4] Yuling Wang, Changxin Tian, Binbin Hu, Yanhua Yu, Ziqi Liu, Zhiqiang Zhang, Jun Zhou, Liang Pang, Xiao Wang. Can Small Language Models be Good Reasoners for Sequential Recommendation? WWW 2024 (CCF-A)

[C5] Yujie Xing, Xiao Wang*, Yibo Li, Hai Huang, Chuan Shi. Less is More: on the Over-Globalizing Problem in Graph Transformers. ICML 2024 (CCF-A)

[C6] Zhibiao Wang, Xiao Wang*, Haoyue Deng, Nian Liu, Shirui Pan, Chunming Hu. Uncovering the Redundancy in Graph Self-supervised Learning Models. NeurIPS 2024 (CCF-A)

[C7] Zixuan Yang, Xiao Wang, Yanhua Yu, Yuling Wang, Kangkang Lu, Zirui Guo, Xiting Qin, Yunshan Ma, Tat-Seng Chua. Hop-based Heterogeneous Graph Transformer. ECAI 2024.

[J1] Pengfei Zhu, Zhe Dong, Jialu, Li, Qinghua Hu, Xiao Wang, Qilong Wang. CCP-GNN: Competitive Covariance Pooling for Improving Graph Neural Networks. IEEE TNNLS.

[J2] Siyuan Yao, Hao Sun, Tian-Zhu Xiang, Xiao Wang, Xiaochun Cao. Hierarchical Graph Interaction Transformer with Dynamic Token Clustering for Camouflaged Object Detection. IEEE TIP. (CCF-A)

  • 2023

[C1] Shaohua Fan, Shuyang Zhang, Xiao Wang, Chuan Shi. Directed acyclic graph structure learning from dynamic graphs. AAAI 2023. (CCF-A)

[C2] Yue Yu, Xiao Wang*, Mengmei Zhang, Nian Liu, Chuan Shi. Provable Training for Graph Contrastive Learning. NeurIPS 2023. (CCF-A) Spotlight

[C3] Donglin Xia, Xiao Wang*, Nian Liu, Chuan Shi. Learning Invariant Representations of Graph Neural Networks via Cluster Generalization. NeurIPS 2023. (CCF-A)

[C4] Xiang Zhuang, Qiang Zhang, Keyan Ding, Yatao Bian, Xiao Wang, Jingsong Lv, Hongyang Chen, Huajun Chen. Learning Invariant Molecular Representation in Latent Discrete Space. NeurIPS 2023. (CCF-A)

[C5] Mengmei Zhang, Xiao Wang, Chuan Shi, Lingjuan Lyu, Tianchi Yang, Junping Du. Minimum Topology Attacks for Graph Neural Networks. WWW 2023. (CCF-A)

[C6] Yuling Wang, Xiao Wang, Xiangzhou Huang, Yanhua Yu, Haoyang Li, Mengdi Zhang, Zirui Guo, Wei Wu. Intent-aware Recommendation via Disentangled Graph Contrastive Learning. IJCAI 2023. (CCF-A)

[C7] Yanbei Liu, Yu Zhao, Xiao Wang*, Geng Lei, Zhitao Xiao. Multi-Scale Subgraph Contrastive Learning. IJCAI 2023. (CCF-A)

[C8] Yizhen Zheng, He Zhang, Vincent Lee, Yu Zheng, Xiao Wang, Shirui Pan. Finding the Missing-half: Graph Complementary Learning for Homophily-prone and Heterophily-prone Graphs. ICML 2023. (CCF-A)

[C9] Shuyun Gu, Xiao Wang, Chuan Shi. Duplicate Multi-modal Entities Detection with Graph Contrastive Self-training Network. ECML-PKDD 2023.

[J1] Meiqi Zhu, Xiao Wang, Chuan Shi, Yibo Li, Junping Du. Towards Adaptive Information Fusion in Graph Convolutional Networks. IEEE TKDE. (CCF-A)

[J2] Nian Liu, Xiao Wang, Hui Han, Chuan Shi. Hierarchical Contrastive Learning Enhanced Heterogeneous Graph Neural Network. IEEE TKDE. (CCF-A)

[J3] Shaohua Fan, Xiao Wang, Chuan Shi, Peng Cui, Bai Wang. Generalizing graph neural networks on out-of-distribution graphs. IEEE TPAMI arXiv:2111.10657 (opens new window) (CCF-A)

[J4] Chuan Shi, Meiqi Zhu, Yue Yu, Xiao Wang*, Junping Du. Unifying Graph Neural Networks with a Generalized Optimization Framework. ACM TOIS. (CCF-A)

[J5] Wei Zhou, Hong Huang, Ruize Shi, Xiran Song, Xue Lin, Xiao Wang, Hai Jin. Temporal Heterogeneous Information Network Embedding via Semantic Evolution. IEEE TKDE. (CCF-A)

[J6] Yanbei Liu, Lianxi Fan, Xiao Wang, Zhitao Xiao, Shuai Ma, Yanwei Pang, Jerry Chun-Wei Lin. HGBER: Heterogeneous Graph Neural Network with Bidirectional Encoding Representation. IEEE TNNLS.

[J7] Yanbei Liu, Shichuan Zhao, Xiao Wang, Lei Geng, Zhitao Xiao, Jerry Chun-Wei Lin. Self-consistent Graph Neural Networks for Semi-supervised Node Classification. IEEE TBD.

[J8] Deyu Bo, Xiao Wang, Yang Liu, Yuan Fang, Yawen Li, Chuan Shi. A Survey on Spectral Graph Neural Networks. arXiv. (opens new window)

  • # 2022

[C1] Deyu Bo, Binbin Hu, Xiao Wang, Zhiqiang Zhang, Chuan Shi, Jun Zhou. Regularizing graph neural networks via consistency-diversity graph augmentations. AAAI 2022. (CCF-A)

[C2] Mengmei Zhang, Xiao Wang, Meiqi Zhu, Chuan Shi, Zhiqiang Zhang, Jun Zhou. Robust heterogeneous graph neural networks against adversarial attacks. AAAI 2022. (CCF-A)

[C3] Nian Liu, Xiao Wang, Lingfei Wu, Yu Chen, Xiaojie Guo, Chuan Shi. Compact Graph Structure Learning via Mutual Information Compression. WWW 2022. (CCF-A)

[C4] Hongrui Liu, Binbin Hu, Xiao Wang, Chuan Shi, Zhiqiang Zhang, Jun Zhou. Confidence May Cheat: Self-Training on Graph Neural Networks under Distribution Shift. WWW 2022. (CCF-A)

[C5] Yugang Ji, Guanyi Chu, Xiao Wang, Chuan Shi, Jianan Zhao. Prohibited Item Detection via Risk Graph Structure Learning. WWW 2022. (CCF-A)

[C6] Yiding Zhang, Chaozhuo Li, Xing Xie, Xiao Wang, Chuan Shi, Yuming Liu, Hao Sun, Liangjie Zhang, Weiwei Deng, Qi Zhang. Geometric disentangled collaborative filtering. SIGIR 2022. (CCF-A)

[C7] Tianyu Zhao, Cheng Yang, Yibo Li, Quan Gan, Zhenyi Wang, Fengqi Liang, Huan Zhao, Yingxia Shao, Xiao Wang, Chuan Shi. Space4HGNN: A novel, modularized and reproducible platform to evaluate heterogeneous graph neural network. SIGIR 2022. (CCF-A)

[C8] Shuyun Gu, Xiao Wang, Chuan Shi, Ding Xiao. Self-supervised graph neural networks for multi-behavior recommendation. IJCAI 2022. (CCF-A)

[C9] Hui Han, Tianyu Zhao, Cheng Yang, Hongyi Zhang, Yaoqi Liu, Xiao Wang, Chuan Shi. OpenHGNN: An open-source toolkit for heterogeneous graph neural networks. CIKM 2022.

[C10] Nian Liu, Xiao Wang*, Deyu Bo, Chuan Shi, Jian Pei. Revisiting graph contrastive learning from the perspective of graph spectrum. NeurIPS 2022. (CCF-A)

[C11] Shaohua Fan, Xiao Wang, Yanhu Mo, Chuan Shi, Jian Tang. Debiasing graph neural networks via learning disentangled causal substructure. NeurIPS 2022. (CCF-A)

[C12] Ruijia Wang, Xiao Wang*, Chuan Shi, Le Song. Uncovering the structural fairness in graph contrastive learning. NeurIPS 2022. (CCF-A)

[J1] Shaohua Fan, Xiao Wang, Chuan Shi, Kun Kuang, Nian Liu, Bai Wang. Debiased graph neural networks with agnostic label selection bias. IEEE TNNLS.

[J2] Zhizhi Yu, Di Jin, Ziyang Liu, Dongxiao He, Xiao Wang, Hanghang Tong, Jiawei Han. Embedding text-rich graph neural networks with sequence and topical semantic structures. KAIS.

[J3] Yanbei Liu, Jinxin Liu, Zhongqiang Wang, Xiao Wang, Fang Zhang, Zhitao Xiao. Binarized network embedding with community structural information. Information Sciences.

  • # 2021

[C1] Jianan Zhao, Xiao Wang*, Chuan Shi, Binbin Hu, Guojie Song, Yanfang Ye. Heterogeneous graph structure learning for graph neural networks. AAAI 2021. (CCF-A)

[C2] Deyu Bo, Xiao Wang, Chuan Shi, Huawei Shen. Beyond Low-frequency Information in Graph Convolutional Networks. AAAI 2021. (CCF-A)

[C3] Houye Ji, Junxiong Zhu, Xiao Wang，Chuan Shi, Bai Wang, Xiaoye Tan, Yanghua Li, Shaojian He. Who You Would Like to Share With? A Study of Share Recommendation in Social E-commerce. AAAI 2021. (CCF-A)

[C4] Yiding Zhang, Xiao Wang, Chuan Shi, Nian Liu, Guojie Song. Lorentzian Graph Convolutional Neural Networks. WWW 2021. (CCF-A)

[C5] Meiqi Zhu, Xiao Wang*, Chuan Shi, Houye Ji, Peng Cui. Interpreting and Unifying Graph Neural Networks with An Optimization Framework. WWW 2021. (CCF-A) Best Paper Awards Nomination! Most influential papers in WWW 2021 (TOP 15) by PaperDigest

[C6] Ruijia Wang, Shuai Mou, Xiao Wang*, Wanpeng Xiao, Qi Ju, Chuan Shi, Xing Xie. Graph Structure Estimation Neural Networks. WWW 2021. (CCF-A)

[C7] Houye Ji, Junxiong Zhu, Chuan Shi, Xiao Wang, Bai Wang, Chaoyu Zhang, Zixuan Zhu, Feng Zhang, Yanghua Li. Large-scale Comb-K Recommendation. WWW 2021. (CCF-A)

[C8] Hong Huang, Ruize Shi, Wei Zhou, Xiao Wang*, Hai Jin, Xiaoming Fu. Temporal heterogeneous information network embedding. IJCAI 2021. (CCF-A)

[C9] Guanyi Chu, Xiao Wang, Chuan Shi, Xunqiang Jiang. CuCo: graph representation with curriculum contrastive learning. IJCAI 2021. (CCF-A)

[C10] Xiao Wang, Nian Liu, Hui Han, Chuan Shi. Self-supervised Heterogeneous Graph Neural Network with Co-contrastive Learning. KDD 2021. (CCF-A)

[C11] Yugang Ji, Chuan Shi, Xiao Wang. Prohibited Item Detection on Heterogeneous Risk Graphs. CIKM 2021.

[C12] Zhizhi Yu, Di Jin, Ziyang Liu, Dongxiao He, Xiao Wang*, Hanghang Tong, Jiawei Han. AS-GCN: Adaptive semantic architecture of graph convolutional networks for text-rich networks. ICDM 2021. Best Student Paper Runner Up!

[C13] Xiao Wang, Hongrui Liu, Chuan Shi, Cheng Yang. Be confident! Towards trustworthy graph neural networks via confidence calibration. NeurIPS 2021. (CCF-A)

[C14] Di Jin, Zhizhi Yu, Cuiying Huo, Rui Wang, Xiao Wang*, Dongxiao He, Jiawei Han. Universal graph convolutional networks. NeurIPS 2021. (CCF-A)

[J1] Houye Ji, Xiao Wang, Chuan Shi, Bai Wang, Philip S. Yu. Heterogeneous graph propagation network. IEEE TKDE. (CCF-A) ESI highly cited paper!

[J2] 石川，王睿嘉，王啸*. 异质信息网络分析与应用综述. 软件学报.

[J3] Yiding Zhang, Xiao Wang, Chuan Shi, Xunqiang Jiang, Yanfang Ye. Hyperbolic graph attention network. IEEE TBD.

[J4] Yiding Zhang, Xiao Wang, Nian Liu, Chuan Shi. Embedding heterogeneous information network in hyperbolic spaces. ACM TKDD.

[J5] Xiao Wang, Deyu Bo, Chuan Shi, Shaohua Fan, Yanfang Ye, Philip S. Yu. A Survey on Heterogeneous Graph Embedding: Methods, Techniques, Applications and Sources. IEEE TBD. ESI highly cited paper!

[J6] Ruijia Wang, Chuan Shi, Tianyu Zhao, Xiao Wang, Yanfang Ye. Heterogeneous information network embedding with adversarial disentangler. IEEE TKDE. (CCF-A)

  • # 2020

[C1] Xiao Wang, Meiqi Zhu, Deyu Bo, Peng Cui, Chuan Shi, Jian Pei. AM-GCN: Adaptive Multi-channel Graph Convolutional Networks. KDD 2020. (CCF-A) Most influential papers in KDD 2020 (TOP 15) by PaperDigest

[C2] Xiao Wang, Ruijia Wang, Chuan Shi, Guojie Song, Qingyong Li. Multi-component graph convolutional collaborative filtering. AAAI 2020. (CCF-A)

[C3] Yanbei Liu, Xiao Wang*, Shu Wu, Zhitao Xiao. Independence promoted graph disentangled networks. AAAI 2020. (CCF-A)

[C4] Xiao Wang, Shaohua Fan, Kuang Kun, Chuan Shi, Jiawei Liu, Bai Wang. Decorrelated clustering with data selection bias. IJCAI 2020. (CCF-A)

[C5] Jianan Zhao, Xiao Wang*, Chuan Shi, Zekuan Liu, Yanfang Ye. Network Schema Preserving Heterogeneous Information Network Embedding. IJCAI 2020. (CCF-A)

[C6] Hong Huang, Zixuan Fang, Xiao Wang*, Youshan Miao, Hai Jin. Motif-preserving temporal network embedding. IJCAI 2020. (CCF-A)

[C7] Deyu Bo, Xiao Wang*, Chuan Shi, Meiqi Zhu, Emiao Lu, Peng Cui. Structural Deep Clustering Network. WWW 2020. (CCF-A) Most influential papers in WWW 2020 (TOP 15) by PaperDigest

[C8] Shaohua Fan, Xiao Wang, Chuan Shi, Emiao Lu, Ken Lin, Bai Wang. One2Multi Graph Autoencoder for Multi-view Graph Clustering. WWW 2020. (CCF-A)

[C9] Mengmei Zhang, Linmei Hu, Chuan Shi, Xiao Wang. Adversarial Label-Flipping Attack and Defense for Graph Neural Networks. ICDM 2020. (CCF-B)

[J1] Xiao Wang, Yuanfu Lu, Chuan Shi, Ruijia Wang, Peng Cui, Shuai Mou. Dynamic Heterogeneous Information Network Embedding with Meta-path based Proximity. IEEE TKDE 2020. (CCF-A) ESI highly cited paper!

  • 2019

[C1] Xiao Wang, Houye Ji, Chuan Shi, Bai Wang, Peng Cui, Philip S. Yu, Yanfang Ye. Heterogeneous Graph Attention Network. WWW 2019. (CCF-A) [code (opens new window)] Most influential papers in WWW 2019 (TOP 15) by PaperDigest. 2022 The world artificial intelligence conference youth outstanding paper nomination award.

[C2] Xiao Wang, Yiding Zhang, Chuan Shi. Hyperbolic heterogeneous information network embedding. AAAI 2019. (CCF-A) [code (opens new window)]

[C3] Yuanfu Lu, Xiao Wang, Chuan Shi, Philip S. Yu, Yanfang Ye. Temporal network embedding with micro- and macro-dynamics. CIKM 2019. (CCF-B)

[J1] Ping Xuan, Tonghui Shen, Xiao Wang, Tiangang Zhang, Weixiong Zhang. Inferring disease-associated microRNAs in heterogeneous networks with node attributes. *IEEE/ACM Transactions on Computational Biology and Bioinformatics. *(CCF-C)

[J2] Ping Xuan, Yangkun Cao, Tiangang Zhang, Xiao Wang, Shuxiang Pan, Tonghui Shen. Drug repositioning through integration of prior knowledge and projections of drugs and diseases. Bioinformatics. (CCF-B)

[J3] Ping Xuan, Hao Sun, Xiao Wang*, Tiangang Zhang, Shuxiang Pan. Inferring the Disease-Associated miRNAs Based on Network Representation Learning and Convolutional Neural Networks. International Journal of Molecular Sciences.

[J4] Liang Yang, Yuexue Wang, Junhua Gu, Xiaochun Cao, Xiao Wang*, Di Jin, Guiguang Ding, Jungong Han, Weixiong Zhang. Autonomous Semantic Community Detection via Adaptively Weighted Low-rank Approximation. ACM Transactions on Multimedia Computing, Communications, and Applications (TOMCCAP). (CCF-B)

[J5] Chuan Shi, Xiaotian Han, Li Song, Xiao Wang*, Senzhang Wang, Junping Du, Philip S. Yu. Deep collaborative filtering with multi-aspect information in heterogeneous networks. IEEE TKDE. (CCF-A)

  • 2018

[C1] Xiao Wang, Ziwei Zhang, Jing Wang, Peng Cui, Shiqiang Yang. Power-law distribution aware trust prediction. IJCAI 2018. (CCF-A) [code (opens new window)]

[C2] Ke Tu, Peng Cui, Xiao Wang*, Fei Wang, Wenwu Zhu. Structural deep embedding for hyper-networks. AAAI 2018. (CCF-A) [code (opens new window)]

[C3] Ziwei Zhang, Peng Cui, Jian Pei, Xiao Wang*, Wenwu Zhu. TIMERS: error-bounded SVD restart on dynamic networks. AAAI 2018. (CCF-A) [code (opens new window)]

[C4] Jing Wang, Feng Tian, Weiwei Liu, Xiao Wang*, Wenjie Zhang, Kenji Yamanishi. Ranking Preserving Nonnegative Matrix Factorization. IJCAI 2018. (CCF-A)

[C5] Ziwei Zhang, Peng Cui, Xiao Wang, Jian Pei, Xuanrong Yao and Wenwu Zhu. Arbitrary-Order Proximity Preserved Network Embedding. KDD 2018. (CCF-A) [code (opens new window)]

[C6] Ke Tu, Peng Cui, Xiao Wang, Philip S. Yu and Wenwu Zhu. Deep Recursive Network Embedding with Regular Equivalence. KDD 2018. (CCF-A) [code (opens new window)]

[C7] Jianxin Ma, Peng Cui, Xiao Wang and Wenwu Zhu. Hierarchical Taxonomy Aware Network Embedding. KDD 2018. (CCF-A) [code (opens new window)]

[C8] Shaohua Fan, Chuan Shi, Xiao Wang. Abnormal event detection via heterogeneous information network embedding. CIKM 2018. (CCF-B) [code (opens new window)]

[C9] Ziwei Zhang, Peng Cui, Haoyang Li, Xiao Wang, Wenwu Zhu. Billion-scale Network Embedding with Iterative Random Projection. ICDM 2018. (CCF-B) [code (opens new window)]

[C10] Hongcui Wang, Erwei Wang, Di Jin, Xiao Wang, etc. Edge content enhanced network embedding. ICTAI 2018. (CCF-C)

[C11] Jing Hu, Changqing Zhang, Xiao Wang, Pengfei Zhu, Zheng Wang and Qinghua Hu. Latent Subspace Representation For Multiclass Classification. PRICAI 2018. (CCF-C)

[J1] Peng Cui, Xiao Wang*, Jian Pei, Wenwu Zhu. A survey on network embedding. IEEE TKDE . (CCF-A) ESI highly cited paper!

[J2] Yanbei Liu, Kaihua Liu, Changqing Zhang, Xiao Wang, Shaona Wang and Zhitao Xiao. Entropy-based active sparse subspace clustering. Multimedia Tools and Applications. (JCR-3)

  • 2017

[C1] Xiao Wang, Peng Cui, Jing Wang, Jian Pei, Wenwu Zhu, Shiqiang Yang. Community Preserving Network Embedding. The 31st AAAI Conference on Artificial Intelligence (AAAI-17). [CODE (opens new window)] (CCF-A) Most influential papers in AAAI 2017 (TOP 15) by PaperDigest

[C2] Jing Wang, Feng Tian, Xiao Wang*, Chang Hong Liu, Hongchuan Yu, Liang Yang. Multi-Component Nonnegative Matrix Factorization. The 26th International Joint Conference on Artificial Intelligence (IJCAI-17). (CCF-A)

[C3] Jing Wang, Feng Tian, Chang Hong Liu, Hongchuan Yu, Xiao Wang*, Xianchao Tang. Robust Nonnegative Matrix Factorization with Ordered Structure Constraints. The 2017 International Joint Conference on Neural Networks (IJCNN 2017). Oral. (CCF-C)

[J1] Xianchao Tang, Tao Xu, Xia Feng, Guoqing Yang, Jing Wang, Qiannan Li, Yanbei Liu, Xiao Wang*. Learning Community Structures: Global and Local Perspectives. Neurocomputing, 2017. (JCR-2)

[J2] Yingli Zhong, Ping Xuan, Xiao Wang, Tiangang Zhang, Jianzhong Li, Yong Liu, Weixiong Zhang. A non-negative matrix factorization based method for predicting disease-associated miRNAs in miRNA-disease bilayer network. Bioinformatics, 2017. (CCF-B)

[J3] Jing Wang, Feng Tian, Chang Hong Liu, Hongchu an Yu, Kun Zhan, Xiao Wang*. Diverse multi-view nonnegative matrix factorization for data representation. IEEE Transactions on Cybernetics, 2017. (JCR-1)

  • 2016

[C1] Xiao Wang, Di Jin, Xiaochun Cao, Liang Yang, Weixiong Zhang. Semantic community detection in large attribute networks. The Thirtieth AAAI Conference on Artificial Intelligence (AAAI-16). [CODE (opens new window)] (CCF-A)

[C2] Jing Wang, Xiao Wang, Feng Tian, Chang Hong Liu, Hongchuan Yu, Yanbei Liu. Adaptive Multi-View Semi-Supervised Nonnegative Matrix Factorization. The 23rd International Conference on Neural Information Processing (ICONIP 2016). (CCF-C)

[C3] Liang Yang, Xiaochun Cao, Dongxiao He, Chuan Wang, Xiao Wang, Weixiong Zhang. Modularity based community detection with deep learning. The 25th International Joint Conference on Artificial Intelligence (IJCAI-16). (CCF-A)

[J1] Jing Wang, Xiao Wang, Feng Tian, Chang Hong Liu, Hongchuan Yu. Constrained Low-Rank Representation for Robust Subspace Clustering. IEEE Transactions on Cybernetics, 2016. (JCR-1)

[J2] Yanbei Liu, Kaihua Liu, Changqing Zhang, Jing Wang, Xiao Wang*. Unsupervised Feature Selection via Diversity-induced Self-representation. Neurocomputing 2016. [CODE (opens new window)] (JCR-2)

[J3] Xianchao Tang, Guoqing Yang, Tao Xu, Xia Feng, Xiao Wang*, Qiannan Li, Yanbei Liu. Link community detection by nonnegative matrix factorization with multi-step similarities. Modern Physics Letters B. 2016.

  • Before 2015

[C1] Jing Wang, Feng Tian, Changhong Liu, Xiao Wang. Robust Semi-supervised Nonnegative Matrix Factorization. The 2015 International Joint Conference on Neural Networks (IJCNN 2015). Accepted as Oral. (CCF-C)

[C2] Xiaochun Cao, Xiao Wang, Di Jin, Yixin Cao, Dongxiao He. The (un)supervised detection of overlapping communities as well as hubs and outliers via (Bayesian) NMF [C]. The 23th International World Wide Web Conference (WWW2014), poster.

[J1] Xiaochun Cao, Xiao Wang, Di Jin, Yixin Cao, Dongxiao He. Identifying overlapping communities as well as hubs and outliers via nonnegative matrix factorization [J]. Scientific Reports 2013, 3, 2993[CODE (opens new window)] (JCR-2)

[J2] Liang Yang, Xiaochun Cao, Di Jin, Xiao Wang, Dan Meng. A Unified Semi-Supervised Community Detection Framework Using Latent Space Graph Regularization. IEEE Trans. on Cybernetics, 2014 (JCR-1)

[J3] Xiaochun Cao, Xiao Wang*, Di Jin, Xiaojie Guo, Xianchao Tang. A Stochastic Model for Detecting Overlapping and Hierarchical Community Structure. PLOS ONE 2015.

[J4] Liang Yang, Di Jin, Xiao Wang, Xiaochun Cao. Active Link Selection for Efficient Semi-supervised Community Detection. Scientific Reports 2015. (JCR-2)

[J5] Xiao Wang, Xiaochun Cao, Di Jin, Yixin Cao, Dongxiao He. The (un)supervsied NMF methods for discovering overlapping communities as well as hubs and outliers in networks. Physica A: Statistical Mechanics and its Applications. 2015. This is the journal extension version of our WWW2014 paper.

*Corresponding author/Equal contribution.

# Honors Awarded

  • 2024 AI 2000 Most Influential Scholar Honorable Mention in AAAI/IJCAI
  • 2024 AI 2000 Most Influential Scholar Honorable Mention in IR and Recommendation
  • 2024 Top 2% Scientists Worldwide 2023 by Stanford University
  • 2023 国家自然科学二等奖
  • 2023 Top 2% Scientists Worldwide 2023 by Stanford University
  • 2023 AI 2000 Most Influential Scholar Honorable Mention in AAAI/IJCAI
  • 2022 中国人工智能学会吴文俊人工智能优秀青年奖
  • 2022 中国电子学会科技进步一等奖
  • 2022 中国教育部自然科学一等奖
  • 2022 Top 2% Scientists Worldwide 2022 by Stanford University
  • 2022 The world artificial intelligence conference youth outstanding paper nomination award
  • 2022 AI 2000 Most Influential Scholar Honorable Mention in AAAI/IJCAI
  • 2021 Top 2% Scientists Worldwide 2022 by Stanford University
  • 2021 ACM China Rising Star Award Nomination
  • 2021 ICDM 2021 Best Student Paper Runner-up
  • 2021 WWW 2021 Best Paper Awards Nomination

# Books/Chapters

  • 石川, 王啸, 胡琳梅. 数据科学导论. 清华大学出版社. 2021. [Link (opens new window)]
  • Chuan Shi, Xiao Wang, Philip S. Yu. Heterogeneous Graph Representation Learning and Applications. Springer. 2022. [Link (opens new window)].
  • 石川，王啸，俞士纶（Philip S. Yu). 异质图表示学习与应用. 机械工业出版社. 2022
  • Peng Cui, Lingfei Wu, Jian Pei, Liang Zhao, Xiao Wang. Chapter 2: Graph Representation Learning [Link (opens new window)]. In Graph Neural Networks: Foundations, Frontiers, and Applications [Link (opens new window)]. Springer. 2022.
  • Chuan Shi, Xiao Wang, Cheng Yang. Advances in Graph Neural Networks. Springer. 978-3-031-16173-5. 2022.
  • 石川，王啸，杨成. 图神经网络前沿. 人民邮电出版社. 2024.

# Professional Experiences

  • Associate Editor:

    IEEE Trans on Artificial Intelligence (IEEE TAI, 2024.01-2024.12)

  • Reviewer for Journals:

    IEEE TPAMI, IEEE TKDE, IEEE TCybernetics, IEEE TNNLS, IEEE TIP, IEEE TBD, IEEE TSP, IEEE TMI, IEEE Transactions on Network Science and Engineering, ACM TOIS, ACM TIST, Journal of Artificial Intelligence Research (JAIR), Information Sciences, Neurocomputing, World Wide Web Journal, Neural Networks, PLOS ONE, Future Generation Computer Systems, MultiMedia Tools and Applications, Frontiers of Computer Science, Computer Science (in Chinese), Information Processing & Management

  • SPC/PC for Conferences (After 2018):

    • PC: AAAI 2018, 2019, 2020; CIKM 2019; IJCAI 2019, 2020, 2022, 2023, 2024; KDD 2019, 2020, 2021, 2022, 2023, 2024; PAKDD 2019, 2020, 2021, 2023, 2024; ACM MM 2019, 2020, 2021, 2024; ECAI 2020; WWW 2021, 2023, 2024; ICDM 2020, 2021, 2022; ICML 2021, 2022, 2023, 2024; ICCV 2021; PRICAI 2021; WSDM 2022, 2023, 2024; CVPR 2022, 2023, 2024; SDM 2022, 2024; ICME 2022; ECCV 2022, 2024; NeurIPS 2022, 2023, 2024; ICLR 2024
    • SPC: AAAI 2021, 2022, 2023, 2024; IJCAI 2021; WWW 2022