Di Jin received the PhD degree in computer science from Jilin University, Changchun, China, in 2012. He was a research scholar in DMG at UIUC during 2019 to 2020. He is currently a Full Professor of computer science, Tianjin University, Tianjin, China. His research interests include graph machine learning, especially on GNNs, network embedding, and community detection. To date, he has published more than 50 research papers in top-tier journals and conferences, including the TKDE, TNNLS, TYCB, AAAI, IJCAI, NeurIPS, and WWW. He currently serves as the Associate Editor in Information Sciences, a PC Board Member in IJCAI 2020-2024, and SPCs in AAAI 2018-2022 and IJCAI 2021-2022. He was the recipient of the Best Paper Award Runner-up of WWW 2021, the Best Student Paper Award Runner-up of ICDM 2021, and Rising Star Award of ACM Tianjin at 2018.
金弟，天津大学智算学部教授，博士生导师。一直从事图机器学习，特别是网络表示学习、社团发现、图神经网络以及电商搜索推荐方面的研究。在本领域顶级期刊或会议上发表论文 50 余篇，获 CCF A 类会议WWW 2021最佳论文奖亚军、国际数据挖掘顶会ICDM 2021最佳学生论文奖亚军、中国社会信息处理大会SMP 2021最佳论文奖、中国计算机教育大会2022最佳论文奖、《自动化学报》年度优秀论文奖，担任中科院一区 SCI 期刊Information Sciences副主编、Nature旗下SSCI期刊 Humanities & Social Sciences Communications 副主编，CCF A 类会议 IJCAI 程序委员会 Board Member、IJCAI/AAAI 高级程序委员会成员 SPC。主持国家自然基金3项、国家重点研发计划子课题2项。获ACM中国天津新兴奖、中国商业联合会科技进步一等奖。
Academic Activities（学术活动）
· Best Paper Award Runner-up, The Web Conference (WWW), 2021
CCF A 类会议 WWW 2021 最佳论文奖亚军
· Best Student Paper Award Runner-up, IEEE ICDM, 2021
国际数据挖掘顶会ICDM 2021最佳学生论文奖亚军
· 中国社会信息处理大会SMP 2021最佳论文奖
· 中国计算机教育大会2022最佳论文奖
· 《自动化学报》年度优秀论文奖
· 2018年度ACM中国天津分会新星奖
· 2019.9~2020.9: 美国UIUC访问学者，合作导师：Prof. Jiawei Han
·2013.12~2014.7: Post-doc researcher in School of Design,
Engineering & Computing, Bournemouth University, UK
博士后 ，英国伯恩茅斯大学，设计、工程与计算机系
· 2010.9~2011.7: Visiting PhD student in HASLab,
Department of Informatics, University of Minho, Braga, Portugal
联合培养博士研究生，葡萄牙米尼奥大学，信息学院HASLab实验室
· 2008.9~2012.6: PhD in College of Computer Science and
Technology, Jilin University, China
博士，吉林大学, 计算机科学与技术学院, 计算机应用技术专业
· 2005.9~2008.7: MS in College of
Computer Science and Technology, Jilin University, China
硕士，吉林大学, 计算机科学与技术学院, 计算机应用技术专业
· 2001.9~2005.7: BS in
College of Computer Science and Technology, Jilin University, China
学士，吉林大学，计算机科学与技术学院，计算机科学与技术专业
· 1. 国家自然基金面上，面向富文本网络表征的鲁棒语义图神经网络新架构研究，2023.1-2026.12，主持
· 2. 横向，知识图谱增强的图神经网络协同过滤建模，2022.1-2022.10，主持
· 3. 计算机软件新技术国家重点实验室开放课题，面向大规模动态异构金融网络的图神经网络与可解释性推理， 2022.6-2024.5，主持
· 4. National Natural Science Foundation of China: Research on accurate semantic community detection in large-scale complex networks with content (61772361). Jan 2018 - Dec 2021. (Project leader)
国家自然科学基金面上项目，61772361、面向大规模、带内容复杂网络的精准语义社团发现研究、2018/01-2021/12，主持
· 5. 国家重点研发计划子课题1，2018/01-2020/12，主持
· 6. 国家重点研发计划子课题2，2018/07-2021/06，主持
· 7. National Natural Science Foundation of China: Research on hybrid node-link partitioning approaches for discovering overlapping communities in complex networks (61303110). Jan 2014 - Dec 2016. (Project leader)
国家自然科学基金青年项目，61303110、结点—链接协同划分的复杂网络重叠社团发现方法研究、2014/01-2016/12，主持
· 8. PhD Programs Foundation of Ministry of Education of China: Research on unified node-link model for detecting overlapping communities in complex networks (20130032120043). Jan 2014 - Dec 2016. (Project leader)
教育部博士点基金新教师类，20130032120043、基于结点与链接统一模型的复杂网络重叠社团检测研究、2014/01-2016/12，主持
· 9. Peiyang Scholar Foundation of Tianjin University: Finding overlapping communities, hubs and outliers together in complex networks (2015XRG-0007). Jan 2015 - Dec 2016. (Project leader)
天津大学北洋学者项目，2015XRG-0007、复杂网络中重叠社团、社团中心和孤立点的同步探测研究、2015/01-2016/12，主持
· 10. Open Project Program of Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education of China: Study on community detection in complex networks based on Markov dynamics (93K172013K02). Jan 2013- Dec 2014. (Project leader)
符号计算与知识工程教育部重点实验室开放课题，基于马尔科夫动力学的复杂网络社区检测方法研究、93K172013K02、2013.1-2014.12，主持
The data and code are available at: https://github.com/jindi-tju, https://github.com/hedongxiao-tju
2023:
[1] Di Jin, Luzhi Wang, Yizhen Zheng, Guojie Song, Fei Jiang, Xiang Li, Wei Lin and Shirui Pan Dual Intent Enhanced Graph Neural Network for Session-based New Item Recommendation, WWW-23
[2] Zhizhi Yu, Di Jin, Cuiying Huo, Zhiqiang Wang, Xiulong Liu, Heng Qi, Jia Wu and Lingfei Wu KGTrust: Evaluating Trustworthiness of SIoT via Knowledge Enhanced Graph Neural Networks, WWW-23
[3] Di Jin, Bingdao Feng, Siqi Guo, Xiaobao Wang, JianguoWei, Zhen Wang. Local-Global Defense Against Unsupervised Adversarial Attacks on Graphs, AAAI-23
[4] Di Jin, Jiayi Shi, Rui Wang, Yawen Li, Yuxiao Huang, Yu-Bin Yang. Trafformer: Unify Time and Space in Traffic, AAAI-23
[5] Cuiying Huo, Di Jin, Yawen Li, Dongxiao He, Yu-Bin Yang, Lingfei Wu. T2-GNN: Graph Neural Networks for Graphs with Incomplete Features and Structure via Teacher-Student Distillation, AAAI-23
[6] XiaobaoWang, Yiqi Dong, Di Jin, Yawen Li, Longbiao Wang, Jianwu Dang, Augmenting Affective Dependency Graph via Iterative Incongruity Graph Learning for Sarcasm Detection, AAAI-23
2022:
[1] Di Jin, Rui Wang, Meng Ge, Dongxiao He, Xiang Li, Wei Lin, Weixiong Zhang, RAW-GNN: RAndom Walk Aggregation based Graph Neural Network, IJCAI-22, Long (3.75%)
[2] Di Jin, Luzhi Wang, YIZHEN ZHENG, Xiang Li, Fei Jiang, Wei Lin, Shirui Pan, CGMN: A Contrastive Graph Matching Network for Self-Supervised Graph Similarity Learning, IJCAI-22, Long (3.75%)
[3] Di Jin, Yingli Gong, Zhiqiang Wang, Zhizhi Yu, Dongxiao He, Yuxiao Huang and Wenjun Wang, Graph Neural Network for Higher-Order Dependency Networks, The Web Conference (WWW-22), Oral
[4] Dongxiao He, Rui Guo, Xiaobao Wang, Di Jin, Yuxiao Huang and Wenjun Wang, Inflation Improves Graph Learning, The Web Conference (WWW-22), Oral
[5] Tao Wang, Di Jin, Rui Wang, Dongxiao He, Yuxiao Huang, Powerful Graph Convolutioal Networks with Adaptive Propagation Mechanism for Homophily and Heterophily, AAAI-22, Oral
[6] Xin Sun, Xin Huang, and Di Jin*, Fast Algorithms for Core Maximization on Large Graphs, PVLDB 2022
[7] Di Jin, Rui Wang, Tao Wang, Dongxiao He, Weiping Ding, Yuxiao Huang, Longbiao Wang, Witold Pedrycz, Amer: A New Attribute-Missing Network Embedding Approach, IEEE Transactions on Cybernetics, 2022
[8] Dongxiao He, Chundong Liang, Cuiying Huo, Zhiyong Feng, Di Jin, Liang Yang, Weixiong Zhang, Analyzing Heterogeneous Networks with Missing Attributes by Unsupervised Contrastive Learning, IEEE Transactions on Neural Networks and Learning Systems (TNNLS), 2022
[9] Z Yu, Di Jin, Z Liu, D He, X Wang, H Tong, J Han. Embedding text-rich graph neural networks with sequence and topical semantic structures, Knowledge and Information Systems, 2022, 1-28
[10] Di Jin, W Wang, G Song, SY Philip, J Han, Guest Editorial: Special Issue on Network Structural Modeling and Learning in Big Data, IEEE Transactions on Big Data, 2022, 8 (04), 867-868
[11] N Jiang, W Jie, J Li, X Liu, Di Jin, GATrust: A Multi-Aspect Graph Attention Network Model for Trust Assessment in OSNs, IEEE Transactions on Knowledge and Data Engineering, 2022
[12] T Li, W Wang, P Jiao, Y Wang, R Ding, H Wu, L Pan, Di Jin, Exploring Temporal Community Structure via Network Embedding, IEEE Transactions on Cybernetics, 2022
[13] X Su, S Xue, F Liu, J Wu, J Yang, C Zhou, W Hu, C Paris, S Nepal, Di Jin, et al, A comprehensive survey on community detection with deep learning, IEEE Transactions on Neural Networks and Learning Systems, 2022
[14] W Lu, N Jiang, Di Jin, H Chen, X Liu, Learning Distinct Relationship in Package Recommendation With Graph Attention Networks, IEEE Transactions on Computational Social Systems, 2022
[15] Na Li, Di Jin, Junhai Xu, Functional brain abnormalities in major depressive disorder using a multiscale community detection approach, Neuroscience, 2022
2021:
[1] Di Jin, Cuiying Huo, Chundong Liang, and Liang Yang, Heterogeneous Graph Neural Network via Attribute Completion. The Web Conference (WWW-21), Oral, Best Paper Award Runner-up (2/1736)
[2] Zhizhi Yu, Di Jin, Ziyang Liu, Dongxiao He, Xiao Wang, Hanghang Tong, and Jiawei Han, AS-GCN: Adaptive Semantic Architecture of Graph Convolutional Networks for Text-Rich Networks, ICDM-21, Oral, Best Student Paper Award Runner-up
[3] Di Jin, Zhizhi Yu, Cuiying Huo, Dongxiao He, Xiao Wang, and Jiawei Han, Universal Graph Convolutional Networks, NeurIPS-21
[4] Di Jin, Xiangchen Song, Zhizhi Yu, Ziyang Liu, Heling Zhang, Zhaomeng Cheng, and Jiawei Han, BiTe-GCN: A New GCN Architecture via Bidirectional Convolution of Topology and Features on Text-rich Networks, WSDM-21, Oral
[5] Di Jin, Zhizhi Yu, Pengfei Jiao, Shirui Pan, Dongxiao He, Jia Wu, Philip S. Yu, and Weixiong Zhang, A Survey of Community Detection Approaches: From Statistical Modeling to Deep Learning, TKDE 2021
[6] Di Jin, Zhizhi Yu, Dongxiao He, Carl Yang, Philip S. Yu, and Jiawei Han, GCN for HIN via Implicit Utilization of Attention and Meta-paths, TKDE, 2021
[7] Di Jin, Xiaobao Wang, Dongxiao He, Jianwu Dang, and Weixiong Zhang, Robust Detection of Link Communities with Summary Description in Social Networks, TKDE, 2021
[8] Xin Sun, Xin Huang, Zitan Sun, and Di Jin*, Budget-constrained Truss Maximization over Large Graphs: A Component-based Approach, CIKM-21, Oral
[9] Dongxiao He, Shuai Li, Di Jin, Pengfei Jiao, Yuxiao Huang. Self-Guided Community Detection on Networks with Missing Edges. IJCAI-21, Oral
[10] Dongxiao He, Tao Wang, Lu Zhai, Di Jin, Liang Yang, Yuxiao Huang, Zhiyong Feng, Philip S. Yu, Adversarial Representation Mechanism Learning for Network Embedding, TKDE, 2021
[11] Pengfei Jiao, Qiang Tian, Wang Zhang, Xuan Guo, Di Jin and Huaming Wu. Role Discovery Guided Network Embedding based on Autoencoder and Attention Mechanism. IEEE Transactions on Cybernetics, 2021.
[12] Liang Yang, Yuanfang Guo, Junhua Gu, Di Jin, Bo Yang, Xiaochun Cao, Probabilistic Graph Convolutional Network via Topology-Constrained Latent Space Model, IEEE Transactions on Cybernetics, 2021
[13] Dongxiao He, Huixin Liu, Zhiyong Feng, Xiaobao Wang, Di Jin, Wenze Song, Yuxiao Huang, A Joint Community Detection Model: Integrating Directed and Undirected Probabilistic Graphical Models via Factor Graph with Attention Mechanism, IEEE Transactions on Big Data (TBD), 2021
[14] Ziyang Liu, Junqing Chen, Yunjiang Jiang, Yue Shang, Wei Xiong, Sulong Xu, Zhaomeng Cheng, Bo Long, Lingfei Wu, Yun Xiao and Di Jin. Semi-Explicit MMoE via Heterogeneous Multi-Task Learning for Ranking Relevance, the IRS2021 KDD workshop, 2021
[15] Ziyang Liu, Zhaomeng Cheng, Yunjiang Jiang, Yue Shang, Wei Xiong, Sulong Xu, Bo Long, & Di Jin. Heterogeneous Network Embedding for Deep Semantic Relevance Match in E-commerce Search. 2021. arXiv:arXiv:2101.04850
2020:
[1] Di Jin, Ge Zhang, Kunzeng Wang, Pengfei Jiao, Dongxiao He, Francoise Fogelman-Soulié, Xin Huang, Detecting Communities with Multiplex Semantics by Distinguishing Background, General and Specialized Topics, TKDE, 2020
[2] Di Jin, Bingyi Li, Pengfei Jiao, Dongxiao He, Weixiong Zhang, Modeling with node popularities for autonomous overlapping community detection, ACM Transactions on Intelligent Systems and Technology (TIST), 2020
[3] Di Jin, Xiaobao Wang, Mengquan Lu, Jianguo Wei, Wenhuan Lu, Francoise Fogelman-Soulié. Detection of generalized semantic communities in social networks, IEEE Transactions on Network Science and Engineering (TNSE), 2020
[4] Di Jin, Rui Li, Junhai Xu, Multiscale Community Detection in Functional Brain Networks Constructed using Dynamic Time Warping, IEEE Transactions on Neural Systems and Rehabilitation Engineering (TNSRE), 2020
[5] Xiaobao Wang, Di Jin*, Katarzyna Musial, Jianwu Dang. Topic Enhanced Sentiment Spreading Model in Social Networks Considering User Interest. AAAI-20, Oral
[6] Liang Yang, Fan Wu, Junhua Gu, Chuan Wang, Xiaochun Cao, Di Jin*, and Yuanfang Guo, Graph Attention Topic Modeling Network, WWW-20, Oral
[7] Dongxiao He, Yue Song, Di Jin*, Zhiyong Feng, Binbin Zhang, Zhizhi Yu, Weixiong Zhang, Community-Centric Graph Convolutional Network for Unsupervised Community Detection, IJCAI-20, Oral
[8] Dongxiao He, Lu Zhai, Zhigang Li, Liang Yang, Di Jin*, Yuxiao Huang, Philip S. Yu, Adversarial Mutual Information Learning for Network Embedding, IJCAI-20, Oral
[9] Yingkui Wang, Di Jin*, Carl Yang, and Jianwu Dang, Integrating Group Homophily and Individual Personality of Topics Can Better Model Network Communities, ICDM-20, Oral
[10] Yunjiang Jiang, Yue Shang, Ziyang Liu, Hongwei Shen, Yun Xiao, Weipeng Yan, and Di Jin, BERT2DNN: BERT Distillation with Massive Unlabeled Data for Online E-Commerce Search, ICDM-20, Oral
[11] Hongyu Shan, Di Jin*, Pengfei Jiao, Ziyang Liu, Bingyi Li, and Yuxiao Huang, NF-VGA: Incorporating Normalizing Flows into Graph Variational Autoencoder for Embedding Attribute Networks, ICDM-20
[12] Liang Yang, Yuangfang Guo, Xiaochun Cao, Chuan Wang, Lu Zhai, Di Jin, and Junhua Gu, Toward Unsupervised Graph Neural Network: Interactive Clustering and Embedding via Optimal Transport, ICDM-20
[13] 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 (TOMM), 2020
2019:
[1] Di Jin, Rui Li, Junhai Xu, Multiscale Community Detection in Functional Brain Networks Constructed using Dynamic Time Warping, IEEE Transactions on Neural Systems and Rehabilitation Engineering, 2019
[2] Di Jin, Bingyi Li, Pengfei Jiao, Dongxiao He, Weixiong Zhang, Network-Specific Variational Auto-Encoder for Embedding in Attribute Networks, IJCAI-19, Oral
[3] Dongxiao He, Wenze Song, Di Jin*, Zhiyong Feng, Yuxiao Huang, An End-to-End Community Detection Model: Integrating LDA into Markov Random Field via Factor Graph, IJCAI-19, Oral
[4] Zesheng Kang, Liang Yang, Yuanfang Guo, Xiaochun Cao, Di Jin, Bo Yang, Optimal Graph Convolutional Network with Learnable Graph, IJCAI-19, Oral
[5] Di Jin, Jiantao Huang, Pengfei Jiao, Liang Yang, Dongxiao He, Françoise Soulie-Fogelman and Yuxiao Huang. A Novel Generative Topic Embedding Model by Introducing Network Communities, WWW-19, short paper
[6] Di Jin, Xinxin You, Weihao Li, Dongxiao He, Weixiong Zhang, Incorporating Network Embedding into Markov Random Field for Better community detection, AAAI-19, Oral
[7] Di Jin, Ziyang Liu, Weihao Li, Dongxiao He, Weixiong Zhang, Markov Random Field Meets Graph Convolutional Network Semi-Supervised Community Detection in Attribute Networks, AAAI-19, Oral
[8] Yingkui Wang, Di Jin*, Jianwu Dang, Community Detection in Social Networks Considering Topic Correlations, AAAI-19, Oral
[9] Xiaobao Wang, Di Jin*, Mengquan Liu, Dongxiao He, Kaska Musial-Gabrys and Jianwu Dang, Emotional Contagion-Based Social Sentiment Mining in Social Networks by Introducing Network Communities, CIKM-19, Oral
[10] 金弟, 尤心心, 刘岳森, 何东晓. 结构特征强化的高效马尔可夫随机场社团发现方法, 计算机学报, 2019
[11] Kuntharrgyal Khysru, Di Jin*, Jianwu Dang. Morphological Verb-Aware Tibetan Language Model, IEEE Access, 2019, 7: 72896-72904, 27 May 2019
[12] Di Jin, Bingyi Li, Pengfei Jiao, Dongxiao He, and Hongyu Shan. Community Detection via Joint Graph Convolutional Network Embedding in Attribute Network. In International Conference on Artificial Neural Networks, Springer, 2019.
[13] Di Jin, Zhigang Li, Liang Yang, Dongxiao He, Pengfei Jiao, Lu Zhai. Adversarial Capsule Learning for Network Embedding. ICTAI-19
[14] Dongxiao He, Yue Song, Di Jin*. A Simple and Effective Community Detection Method Combining Network Topology with Node Attributes. The 12th International Conference on Knowledge Science, Engineering and Management (KSEM 2019), Athens, Greece, August 28-30, 2019.
[15] Hongcui Wang, Shanshan Liu, Di Jin*, Lantian Li, Jianwu Dang, Scalable Community Identification with Manifold Learning on Speaker I-vector Space, IEICE Transactions on Information and Systems, 2019
[16] 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 (TOMM), 2019
2018:
[1] Di Jin, Meng Ge, Liang Yang, Longbiao Wang, Dongxiao He, Weixiong Zhang, Integrative Network Embedding via Deep Joint Reconstruction, IJCAI-18 (CCF A类会议长文)
[2] Ge Zhang, Di Jin*, Jian Gao, Pengfei Jiao, Francoise Fogelman-Soulié, Xin Huang, Finding Communities with Hierarchical Semantics by Distinguishing General and Specialized Topics, IJCAI-18 (CCF A类会议长文)
[3] Liang Yang, Yanfang Guo, Di Jin*, Xiaochun Cao, 3-in-1 Correlated Embedding via Adaptive Exploration of the Structure and Semantic Subspaces, IJCAI-18 (CCF A类会议长文)
[4] Di Jin, Xiaobao Wang, Ruifang He, Dongxiao He, Jianwu
Dang, Weixiong Zhang. Robust Detection of Link Communities in Large Social
Networks by Exploiting Link Semantics, AAAI-18 (CCF A类会议长文)
[5] Dongxiao He, Xinxin You, Zhiyong Feng, Di Jin, Xue Yang, Weixiong Zhang. A Network-Specific Markov Random Field Approach to Community Detection, AAAI-18 (CCF A类会议长文)
[6] 金弟, 刘子扬, 贺瑞芳, 王啸, 何东晓, 面向带属性复杂网络的鲁棒、强解释性社团发现方法, 计算机学报, 录用, 2018 (国家一级期刊)
[7] Wenjun Wang, Xiao Liu, Pengfei Jiao, Xue Chen, and Di Jin*, A Unified Weakly Supervised Framework for Community Detection and Semantic Matching, PAKDD-18 (EI, CCF C类会议)
[8] Ruifang He, Xuefei Zhang, Di Jin*, LongbiaoWang, Jianwu Dang, Xiangang Li. Interaction-Aware Topic Model for Microblog Conversations through Network Embedding and User Attention, COLING-18.
[9] Fengyu Guo, Ruifang He, Di Jin, Jianwu Dang, LongbiaoWang, Xiangang Li. Implicit Discourse Relation Recognition using Neural Tensor Network with Interactive Attention and Sparse Learning, COLING-18.
[10] Di Jin, Ziyang Liu, Ruifang He, Xiao Wang, Dongxiao He. Robust Detection of Communities with Multi-Semantics in Large Attributed Networks. KSEM-18.
[11] Jinxin Cao, Di Jin, Jianwu Dang, Autoencoder Based Community Detection with Adaptive Integration of Network Topology and Node Contents. KSEM-18.
[12] Hongcui Wang, Erwei Wang, Di Jin, Xiao Wang, Jing Wang and Dongxiao He. Edge Content Enhanced Network Embedding, ICTAI-18.
[13] Meng Qin, Di Jin*, Kai Lei*, Bogdan Gabrys, Katarzyna Musial. Adaptive Community Detection Incorporating Topology and Content in Social Networks. Knowledge-Based Systems, 2018.
[14] Jinxin Cao, Di Jin*, Liang Yang, Jianwu Dang. Incorporating network structure with node contents for Community Detection on large networks using deep learning. Neurocomputing, 297(2018)71-81.
[15] Jinxin Cao, Hongcui Wang, Di Jin, Jianwu Dang, Combination of links and node contents for community discovery using a graph regularization approach, Future Generation Computer Systems, 2018.
[16] Pengfei Jiao, Wenjun Wang, Di Jin. Constrained common cluster based model for community detection in temporal and multiplex networks. Neurocomputing, 2018, 275: 768-780.
2017:
[2] Di Jin, Xiaobao Wang, Dongxiao He, Wenhuan Lu,
Francoise Fogelman-Soulié, Jianwu Dang. Identification of generalized
communities with semantics in networks with content. ICTAI’17 (EI, CCF
C类会议)
[3] Di Jin, Meng Ge, Zhixuan Li, Wenhuan Lu, Dongxiao He,
Francoise Fogelman-Soulie. Using Deep Learning for Community Discovery in Large
Social Networks. ICTAI’17 (EI, CCF C类会议)
[4] Limengzi Yuan, Wenjun Wang, Pengfei Jiao, Di Jin*, Wenxin
Wei. Tracking and detecting dynamic communities with node popularity
preservation. ICTAI’17 (EI, CCF C类会议)
[5] Meng Qin, Di Jin*, Dongxiao He, Bogdan Gabrys, Katarzyna Musial.
Adaptive Community Detection Incorporating Topology and Content in Social
Networks. 2017 IEEE/ACM International Conference on Advances in Social Networks
Analysis and Mining, pp. 675-682 (EI)
[6] Wenjun Wang, Xue Chen, Pengfei jiao, Di Jin. Similarity-based Regularized Latent Feature Model for Link Prediction in Bipartite Networks. Scientific Reports, 2017 (SCI二区, IF: 5.018)
[7] Xiao Liu, Wenjun Wang, Dongxiao He, Pengfei Jiao, Di Jin, Carlo Vittorio Cannistraci, Semi-supervised community detection based on non-negative matrix factorization with node popularity, Information Sciences, Volume 381, March 2017, Pages 304-321 (SCI二区, IF: 3.858 )
[8] Liang Yang, Xiaochun Cao, Di Jin, Dongxiao He, Francoise Fogelman-Soulie, Huazhu Fu, Improving the Efficiency and Effectiveness of Community Detection via Prior-Induced Equivalent Super-Network, Scientific Reports, 2017.03.29, 7, 634
[9] Liang Yang, Xiaochun Cao, Meng Ge, Di Jin, Dongxiao He, Huazhu Fu, Jing Wang, Exploring the roles of cannot-link constraint in community detection via Multi-variance Mixed Gaussian Generative Model, PLos One, 2017.07.5, 12(7), e0178029
2016:
[1] Di Jin, Hongcui Wang, Jianwu Dang, Dongxiao He, Weixiong
Zhang. Detect overlapping communities via modeling and ranking
node popularities. AAAI-16, pp.172-178 (CCF A类会议长文)
[2] Xiao Wang, Di Jin, Xiaochun Cao, Liang Yang, Weixiong Zhang.
Semantic Community Identification in Large Attribute Networks. AAAI-16,
pp.265-271 (CCF A类会议长文)
[3] Xiao Wang, Xiaochun Cao, Di Jin*, Yixin
Cao, Dongxiao He. The (un)supervised NMF methods for discovering overlapping
communities as well as hubs and outliers in networks. Physica A: Statistical
Mechanics and its Applications. 2016, Volume 446, 15 March 2016, Pages 22–34 (SCI三区, IF: 1.732)
[4] Dongxiao He, Hongcui Wang, Di Jin, Baolin Liu, A model framework for the enhancement of community
detection in complex networks, Physica A: Statistical Mechanics and its
Applications, Volume 461, 1 November 2016, Pages 602–612 (SCI三区, IF: 1.732)
[5] Wenjun Wang, Pengfei Jiao, Dongxiao He, Di Jin, Lin Pan, Bogdan Gabrys. Autonomous Overlapping Community Detection in Temporal Networks: A Dynamic Bayesian Nonnegative Matrix Factorization Approach. Knowledge Based Systems, Volume 110, 15 October 2016, Pages 121–134 (SCI二区, IF: 3.325)
2015:
[1] Di Jin, Zheng Chen, Dongxiao He, Weixiong Zhang.
Modeling with node degree preservation can accurately find communities, AAAI-15,
Jan 25-30, 2015 (CCF A类会议长文)
[2] Dongxiao He, Dayou Liu, Di Jin, Weixiong Zhang. A stochastic model for the detection of heterogeneous link communities in complex networks, AAAI-15, Jan 25-30, 2015 (CCF A类会议长文)
[3] Di
Jin,
Bogdan Gabrys, Jianwu Dang. Combined node and link partitions method for finding
overlapping communities in complex networks. Scientific Reports,
2015,
5,
8600. (SCI二区, IF: 5.078)
[4]
Liang
Yang,
Di
Jin*,
Xiao
Wang,
Xiaochun
Cao.
Active link selection for efficient semi-supervised community detection.
Scientific Reports,
2015,
5,
9039.
(SCI二区,
IF: 5.078)
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本科生课程: 《数据结构》、《人工智能基础》
博士生课程: 《智能科学》
1. 已毕业硕士生张鸽、在读博士生于智郅分别获国际数据挖掘顶会ICDM 2021最佳学生论文冠亚军
2. 王晓宝入选“百度学术AI华人新星百强榜单”，获校优秀博士论文和校优秀毕业生
3. 硕士生黄剑涛、李炳艺、李志刚组队获“中电慧治杯政府治理大数据应用算法大赛”冠军（奖金5万元）
4. 硕士生王涛和孙新获校优秀硕士论文，均为外审全A（该年度220名硕士生中共8位外审全A）
5. 博士生于智郅和硕士生李炳艺获国奖
6. 刘子扬、尤心心、张鸽、李炳艺、李帅、覃孟获校优秀毕业生
7. 宋文卓荣获国家级大创《大规模网络的深度表征及社团发现方法》优秀结题
8. 硕士毕业生刘子扬获京东2019年度最佳新人奖
9. 硕士毕业生张鸽获2022 Microsoft Research PhD Fellows（2022年澳洲唯一一位）