We study the problem of clustering nodes in a dynamic graph, where the connections between nodes and nodes' cluster memberships may change over time, e.g., due to community migration. We first propose a dynamic stochastic block model that captures these changes, and a simple decay-based clustering algorithm that clusters nodes based on weighted connections between them, where the weight decreases at a fixed rate over time. This decay rate can then be interpreted as signifying the importance of including historical connection information in the clustering. However, the optimal decay rate may differ for clusters with different rates of turnover. We characterize the optimal decay rate for each cluster and propose a clustering method that achieves almost exact recovery of the true clusters. We then demonstrate the efficacy of our clustering algorithm with optimized decay rates on simulated graph data. Recurrent neural networks (RNNs), a popular algorithm for sequence learning, use a similar decay-based method, and we use this insight to propose two new RNN-GCN (graph convolutional network) architectures for semi-supervised graph clustering. We finally demonstrate that the proposed architectures perform well on real data compared to state-of-the-art graph clustering algorithms.
Unveiling Global Interactive Patterns across Graphs: Towards Interpretable Graph Neural Networks
School of Software Technology, Zhejiang University（浙江大学软件学院）；Hangzhou High-Tech Zone (Binjiang) Institute of Blockchain and Data Security（杭州高新区（滨江）区块链与数据安全研究院）；Big Graph Center, School of Computer and Computing Science, Hangzhou City University（杭州城市大学计算机与计算科学学院大图中心）；College of Computer Science and Technology, Zhejiang University（浙江大学计算机科学与技术学院）
School of Software, Dalian University of Technology（大连理工大学软件学院）；Key Laboratory for Ubiquitous Network and Service Software of Liaoning Province（辽宁省泛在网络与服务软件重点实验室）
Interpretable clustering via optimal multi-way decision trees
Graduate School of Science and Technology, University of Tsukuba（科学技术研究生院，东京大学）；Institute of Systems and Information Engineering, University of Tsukuba（系统与信息工程研究所，东京大学）
College of Information Science and Engineering, Henan University of Technology（河南理工大学信息科学与工程学院）；School of Software, Dalian University of Technology（大连理工大学软件学院）；Xinchang Power Supply Company, State Grid Corporation of China（国网浙江绍兴供电公司）
Interpretable Parallel Recurrent Neural Networks with Convolutional Attentions for Multi-Modality Activity Modeling
School of Computer Science and Engineering, UNSW Sydney, Australia（澳大利亚新南威尔士大学计算机科学与工程学院）；School of Information Technology, RMIT University, Australia（澳大利亚皇家墨尔本理工大学信息技术学院）；School of Computer Science, Northwestern Polytechnical University, China（中国西北工业大学计算机学院）；School of Software, Tsinghua University China（中国清华大学软件学院）
GARNN: An Interpretable Graph Attentive Recurrent Neural Network for Predicting Blood Glucose Levels via Multivariate Time Series
Institute of Health Informatics, University College London（伦敦大学学院健康信息学研究所）；Department of Psychiatry, University of Oxford（牛津大学精神病学系）；Department of Diabetes & Endocrinology, University College London Hospitals（伦敦大学学院医院糖尿病与内分泌科）；Centre for Bio-Inspired Technology（仿生技术中心）
Clustering and Recognition of Spatiotemporal Features through Interpretable Embedding of Sequence to Sequence Recurrent Neural Networks
Department of Electrical & Computer Engineering（电气与计算机工程系）；Department of Applied Mathematics（应用数学系）；University of Washington（华盛顿大学）；Seattle, WA 98195（西雅图, WA 98195（注意：地址信息按要求不应提取，此处仅为示例说明））
MDL-GBG: A Non-parametric and Interpretable Granular-Ball Generation Method for Clustering
Department of Mathematics and Computer Science, Gannan Normal University（赣南师范大学数学与计算机科学学院）；Key Laboratory of Data Science and Artificial Intelligence of Jiangxi Education Institutes, Gannan Normal University（江西省教育研究院数据科学与人工智能重点实验室）；Department of Computer Science and Technology, Tongji University（同济大学计算机科学与技术学院）；Department of Electrical and Computer Engineering, University of Alberta（阿尔伯塔大学电气与计算机工程学院）
A hybrid transformer and attention based recurrent neural network for robust and interpretable sentiment analysis of tweets
Khulna University of Engineering & Technology (KUET)（库尔纳工程技术大学）；Advanced Machine Intelligence Research (AMIR) Lab（高级机器智能研究实验室）；American International University-Bangladesh（孟加拉国美国国际大学）；Chowagiken Corp.（Chowagiken公司）；University of Asia Pacific（亚太大学）；University of Aizu（会津大学）
School of Wangzheng Microelectronics, Changzhou University（常州大学王正微电子学院）；School of Electrical Engineering and Automation, Changshu Institute of Technology（常熟理工学院电气与自动化工程学院）
RetainVis: Visual Analytics with Interpretable and Interactive Recurrent Neural Networks on Electronic Medical Records
IBM T.J. Watson Research Center（IBM托马斯·J·沃森研究中心）；Korea University（高丽大学）；Georgia Institute of Technology（佐治亚理工学院）；Chung-Ang University（中央大学）；Catholic University of Daegu（大邱天主教大学）
CoHiRF: Hierarchical Consensus for Interpretable Clustering Beyond Scalability Limits
CEREMADE, University Paris Dauphine-PSL, France（CEREMADE，巴黎大学达菲涅-PSL大学，法国）；CMAP, École Polytechnique, France（CMAP，巴黎高等理工学院，法国）；Istituto Italiano di Tecnologia, Genova, Italy, University of Novi Sad, Serbia（意大利技术研究所，热那亚，意大利，诺维萨德大学，塞尔维亚）
Interpretable Spectral Variational AutoEncoder (ISVAE) for time series clustering
Universidad Carlos III de Madrid（马德里卡洛斯三世大学）；Instituto de Investigación Sanitaria Gregorio Marañón（格雷戈里奥·马拉尼翁卫生研究所）；Evidence Based Behaviour (eB2)（循证行为（eB2））；Oxford-Man Institute of Quantitative Finance, University of Oxford（牛津大学牛津-曼氏定量金融研究所）
