This paper aims to unify spatial dependency and temporal dependency in a non-Euclidean space while capturing the inner spatial-temporal dependencies for traffic data. For spatial-temporal attribute entities with topological structure, the space-time is consecutive and unified while each node's current status is influenced by its neighbors' past states over variant periods of each neighbor. Most spatial-temporal neural networks for traffic forecasting study spatial dependency and temporal correlation separately in processing, gravely impaired the spatial-temporal integrity, and ignore the fact that the neighbors' temporal dependency period for a node can be delayed and dynamic. To model this actual condition, we propose TraverseNet, a novel spatial-temporal graph neural network, viewing space and time as an inseparable whole, to mine spatial-temporal graphs while exploiting the evolving spatial-temporal dependencies for each node via message traverse mechanisms. Experiments with ablation and parameter studies have validated the effectiveness of the proposed TraverseNet, and the detailed implementation can be found from https://github.com/nnzhan/TraverseNet.
Nara Institute of Science and Technology（奈良先端科学技术大学院大学）
Graph neural networks (GNNs) have been widely used to learn vector representation of graph-structured data and achieved better task performance than conventional methods. The foundation of GNNs is the message passing procedure, which propagates the information in a node to its neighbors. Since this procedure proceeds one step per layer, the range of the information propagation among nodes is small in the lower layers, and it expands toward the higher layers. Therefore, a GNN model has to be deep enough to capture global structural information in a graph. On the other hand, it is known that dee…
Semi-supervised learning on graphs is a widely applicable problem in network science and machine learning. Two standard algorithms -- label propagation and graph neural networks -- both operate by repeatedly passing information along edges, the former by passing labels and the latter by passing node features, modulated by neural networks. These two types of algorithms have largely developed separately, and there is little understanding about the structure of network data that would make one of these approaches work particularly well compared to the other or when the approaches can be meaningfu…
School of Computing, National University of Singapore（新加坡国立大学计算学院）；Institute of Data Science, National University of Singapore（新加坡国立大学数据科学研究所）；Institute for Infocomm Research, A*STAR（新加坡科技研究局资讯通信研究院）
Many well-established anomaly detection methods use the distance of a sample to those in its local neighbourhood: so-called `local outlier methods', such as LOF and DBSCAN. They are popular for their simple principles and strong performance on unstructured, feature-based data that is commonplace in many practical applications. However, they cannot learn to adapt for a particular set of data due to their lack of trainable parameters. In this paper, we begin by unifying local outlier methods by showing that they are particular cases of the more general message passing framework used in graph neu…
Message-passing algorithms have emerged as powerful techniques for approximate inference in graphical models. When these algorithms converge, they can be shown to find local (or sometimes even global) optima of variational formulations to the inference problem. But many of the most popular algorithms are not guaranteed to converge. This has lead to recent interest in convergent message-passing algorithms. In this paper, we present a unified view of convergent message-passing algorithms. We present a simple derivation of an abstract algorithm, tree-consistency bound optimization (TCBO) that is …
Beijing University of Posts and Telecommunications（北京邮电大学）；Tsinghua University（清华大学）
Graph Neural Networks (GNNs) have received considerable attention on graph-structured data learning for a wide variety of tasks. The well-designed propagation mechanism which has been demonstrated effective is the most fundamental part of GNNs. Although most of GNNs basically follow a message passing manner, litter effort has been made to discover and analyze their essential relations. In this paper, we establish a surprising connection between different propagation mechanisms with a unified optimization problem, showing that despite the proliferation of various GNNs, in fact, their proposed p…
A Unifying Relational Perspective on Expressive Lottery Tickets
Faculty of Computer Science, University of Vienna（维也纳大学计算机科学学院）；Doctoral School Computer Science, University of Vienna（维也纳大学计算机科学博士学院）；CeMM Research Center for Molecular Medicine of the Austrian Academy of Sciences（奥地利科学院CeMM分子医学研究中心）；CISPA Helmholtz Center for Information Security（CISPA亥姆霍兹信息安全中心）；School of Electronic Engineering and Computer Science, Queen Mary University of London（伦敦大学玛丽女王学院电子工程与计算机科学学院）；Research Network Data Science, University of Vienna（维也纳大学数据科学研究网络）
On the Rademacher Complexity of Graph Neural Networks: Unifying Expressivity and Geometry
University of Fribourg（弗里堡大学）；Johns Hopkins University（约翰·霍普金斯大学）；Université Paris-Saclay（巴黎萨克雷大学）；University of Ferrara（费拉拉大学）；Technical University of Munich（慕尼黑技术大学）；University of Siena（锡耶纳大学）
Demystifying Topological Message-Passing with Relational Structures: A Case Study on Oversquashing in Simplicial Message-Passing
Max Planck Institute for Mathematics in the Sciences（马克斯·普朗克数学研究所）；UCLA（加州大学洛杉矶分校）；Center for Scalable Data Analytics and Artificial Intelligence（可扩展数据与人工智能中心）；University of Oxford（牛津大学）
Unifying AMP Algorithms for Rotationally-Invariant Models
Institute of Computational Mathematics and Scientific/Engineering Computing, Academy of Mathematics and Systems Science, Chinese Academy of Sciences（中国科学院数学与系统科学研究院计算数学与科学工程计算研究所）
Label Propagation (LPA) and Graph Convolutional Neural Networks (GCN) are both message passing algorithms on graphs. Both solve the task of node classification but LPA propagates node label information across the edges of the graph, while GCN propagates and transforms node feature information. However, while conceptually similar, theoretical relation between LPA and GCN has not yet been investigated. Here we study the relationship between LPA and GCN in terms of two aspects: (1) feature/label smoothing where we analyze how the feature/label of one node is spread over its neighbors; And, (2) fe…
Limits of message passing for node classification: How class-bottlenecks restrict signal-to-noise ratio
Department of Mathematics（数学系）；Department of Computing（计算系）；UKRI Centre for Doctoral Training in AI for Healthcare（英国研究与创新人工智能医疗健康博士培训中心）；Imperial College London（帝国理工学院）；MIT Institute for Data, Systems, and Society（MIT数据、系统与社会研究所）；I-X Centre for AI in Science（I-X人工智能科学中心）；EPSRC Centre for the Mathematics of Precision Healthcare（工程与物理科学研究委员会精准医疗数学中心）
