Contrastive Sequential Interaction Network Learning on Co-Evolving Riemannian Spaces
North China Electric Power University（华北电力大学）；Beijing University of Posts and Telecommunications（北京邮电大学）；University of California, Davis（加州大学戴维斯分校）；State Grid Handan Electric Power Supply Company（国网邯郸供电公司）；Shijiazhuang Institute of Railway Technology（石家庄铁路职业技术学院）；University of Illinois at Chicago（伊利诺伊大学芝加哥分校）
Li Sun, Junda Ye, Jiawei Zhang, Yong Yang, Mingsheng Liu, Feiyang Wang, Philip S. Yu
The sequential interaction network usually find itself in a variety of applications, e.g., recommender system. Herein, inferring future interaction is of fundamental importance, and previous efforts are mainly focused on the dynamics in the classic zero-curvature Euclidean space. Despite the promising results achieved by previous methods, a range of significant issues still largely remains open: On the bipartite nature, is it appropriate to place user and item nodes in one identical space regardless of their inherent difference? On the network dynamics, instead of a fixed curvature space, will the representation spaces evolve when new interactions arrive continuously? On the learning paradigm, can we get rid of the label information costly to acquire? To address the aforementioned issues, we propose a novel Contrastive model for Sequential Interaction Network learning on Co-Evolving RiEmannian spaces, CSINCERE. To the best of our knowledge, we are the first to introduce a couple of co-evolving representation spaces, rather than a single or static space, and propose a co-contrastive learning for the sequential interaction network. In CSINCERE, we formulate a Cross-Space Aggregation for message-passing across representation spaces of different Riemannian geometries, and design a Neural Curvature Estimator based on Ricci curvatures for modeling the space evolvement over time. Thereafter, we present a Reweighed Co-Contrast between the temporal views of the sequential network, so that the couple of Riemannian spaces interact with each other for the interaction prediction without labels. Empirical results on 5 public datasets show the superiority of CSINCERE over the state-of-the-art methods.
Motif-aware Riemannian Graph Neural Network with Generative-Contrastive Learning
North China Electric Power University（华北电力大学）；Beijing University of Posts and Telecommunications（北京邮电大学）；Beihang University（北京航空航天大学）；University of Illinois at Chicago（伊利诺伊大学芝加哥分校）
North China Electric Power University（华北电力大学）；Beijing University of Posts and Telecommunications（北京邮电大学）；Beihang University（北京航空航天大学）；University of Illinois at Chicago（伊利诺伊大学芝加哥分校）
Contrastive Masked Autoencoders for Character-Level Open-Set Writer Identification
University of Technology Sydney（悉尼科技大学）；Australian AI Institute（澳大利亚人工智能研究所）；School of Computer Science, Faculty of Engineering and Information Technology, University of Technology Sydney（悉尼科技大学工程与信息技术学院计算机科学学院）
AMCEN: An Attention Masking-based Contrastive Event Network for Two-stage Temporal Knowledge Graph Reasoning
Institute of Automation, Chinese Academy of Sciences（中国科学院自动化研究所）；University of Chinese Academy of Sciences（中国科学院大学）；Anhui University（安徽大学）；Qingdao Academy of Intelligent Industries（青岛智能产业技术研究院）；Zhejiang Lab（之江实验室）
SLSREC: Self-Supervised Contrastive Learning for Adaptive Fusion of Long- and Short-Term User Interests
School of Artificial Intelligence, Shenzhen University（深圳大学人工智能学院）；Shenzhen Technology University（深圳技术大学）；College of Computer Science, Chongqing University（重庆大学计算机学院）
A Self-supervised Mixed-curvature Graph Neural Network
North China Electric Power University（华北电力大学）；Beijing University of Posts and Telecommunications（北京邮电大学）；Beihang University（北京航空航天大学）；University of California, Davis（加州大学戴维斯分校）；University of Illinois at Chicago（伊利诺伊大学芝加哥分校）
Timestamp-Aware Spatio-Temporal Graph Contrastive Learning for Network Intrusion Detection
Central South University of Forestry and Technology, School of Computer Science and Mathematics（中央林业科技大学计算机科学与数学学院）；Central South University, School of Computer Science and Engineering（中南大学计算机科学与工程学院）
RCoCo: Contrastive Collective Link Prediction across Multiplex Network in Riemannian Space
North China Electric Power University（华北电力大学）；State Grid Handan Electric Power Supply Company（国网邯郸供电公司）；Capital Normal University（首都师范大学）；Beijing Institute of Electronic Science and Technology（北京电子科技学院）；Anhui University of Science and Technology（安徽理工大学）；Beihang University（北京航空航天大学）
A Self-supervised Riemannian GNN with Time Varying Curvature for Temporal Graph Learning
North China Electric Power University（华北电力大学）；Beijing University of Posts and Telecommunications（北京邮电大学）；Beihang University（北京航空航天大学）；University of Illinois at Chicago（伊利诺伊大学芝加哥分校）
Self-Supervised Continual Graph Learning in Adaptive Riemannian Spaces
North China Electric Power University（华北电力大学）；Beijing University of Posts and Telecommunications（北京邮电大学）；Beihang University（北京航空航天大学）；University of Illinois at Chicago（伊利诺伊大学芝加哥分校）
Stable Robot Motions on Manifolds: Learning Lyapunov-Constrained Neural Manifold ODEs
Department of Computer and Information Sciences University of Konstanz（计算机与信息科学系康斯坦茨大学）；Department of Robotics, Mohamed bin Zayed University of Artificial Intelligence（机器人学系Mohamed bin Zayed人工智能大学）；Mechanical Engineering Department, New York University Abu Dhabi（机械工程系纽约大学阿布扎克分校）
Intrinsic-Metric Physics-Informed Neural Networks (IM-PINN) for Reaction-Diffusion Dynamics on Complex Riemannian Manifolds
Department of Bio-Functions and Systems Science（生物功能与系统科学系）；Graduate School of Bio-Applications and Systems Engineering（生物应用与系统工程研究生学校）；Department of Mathematics（数学系）；Faculty of Mathematics and Natural Sciences（数学与自然科学学院）；Universitas Padjadjaran（帕迪贾兰大学）；Department of Informatics（信息系）；Faculty of Science and Informatics（科学与信息学院）；Jenderal of Achmad Yani University（阿赫马德·亚尼大学）
