Papers Graph Classification
“Graph Classification” 태그가 달린 논문 1,025편 · 필터 해제
GraphMDN: Leveraging graph structure and deep learning to solve inverse problems
The recent introduction of Graph Neural Networks (GNNs) and their growing popularity in the past few years has enabled the application of deep learning algorithms to non-Euclidean, graph-structured data. GNNs have achiev…
Graph ClassificationMulti-Hypotheses 3D Human Pose EstimationPose EstimationregressionFewer is More: A Deep Graph Metric Learning Perspective Using Fewer Proxies
Deep metric learning plays a key role in various machine learning tasks. Most of the previous works have been confined to sampling from a mini-batch, which cannot precisely characterize the global geometry of the embeddi…
General ClassificationGraph ClassificationMetric LearningCo-embedding of Nodes and Edges with Graph Neural Networks
Graph, as an important data representation, is ubiquitous in many real world applications ranging from social network analysis to biology. How to correctly and effectively learn and extract information from graph is esse…
BIG-bench Machine LearningGraph ClassificationGraph EmbeddingGraph Learning+5Line Graph Neural Networks for Link Prediction
We consider the graph link prediction task, which is a classic graph analytical problem with many real-world applications. With the advances of deep learning, current link prediction methods commonly compute features fro…
ClassificationGeneral ClassificationGraph ClassificationLink Prediction+2Robust Optimization as Data Augmentation for Large-scale Graphs
Data augmentation helps neural networks generalize better by enlarging the training set, but it remains an open question how to effectively augment graph data to enhance the performance of GNNs (Graph Neural Networks). W…
Data AugmentationGraph ClassificationLink PredictionNode Classification+2Topology-Aware Graph Pooling Networks
Pooling operations have shown to be effective on computer vision and natural language processing tasks. One challenge of performing pooling operations on graph data is the lack of locality that is not well-defined on gra…
Graph ClassificationK-plex Cover Pooling for Graph Neural Networks
We introduce a novel pooling technique which borrows from classical results in graph theory that is non-parametric and generalizes well to graphs of different nature and connectivity pattern. Our pooling method, named K…
Graph ClassificationFast Graph Kernel with Optical Random Features
The graphlet kernel is a classical method in graph classification. It however suffers from a high computation cost due to the isomorphism test it includes. As a generic proxy, and in general at the cost of losing some in…
Graph ClassificationLocality Preserving Dense Graph Convolutional Networks with Graph Context-Aware Node Representations
Graph convolutional networks (GCNs) have been widely used for representation learning on graph data, which can capture structural patterns on a graph via specifically designed convolution and readout operations. In many …
ClassificationDecoderGeneral ClassificationGraph Classification+1Multivariate Time Series Classification with Hierarchical Variational Graph Pooling
With the advancement of sensing technology, multivariate time series classification (MTSC) has recently received considerable attention. Existing deep learning-based MTSC techniques, which mostly rely on convolutional or…
ClassificationDecoderGeneral ClassificationGraph Classification+6Graph Information Bottleneck for Subgraph Recognition
Given the input graph and its label/property, several key problems of graph learning, such as finding interpretable subgraphs, graph denoising and graph compression, can be attributed to the fundamental problem of recogn…
DenoisingGraph ClassificationGraph LearningTowards Expressive Graph Representation
Graph Neural Network (GNN) aggregates the neighborhood of each node into the node embedding and shows its powerful capability for graph representation learning. However, most existing GNN variants aggregate the neighborh…
Graph ClassificationGraph Neural NetworkGraph Representation LearningRepresentation LearningFactorizable Graph Convolutional Networks
Graphs have been widely adopted to denote structural connections between entities. The relations are in many cases heterogeneous, but entangled together and denoted merely as a single edge between a pair of nodes. For ex…
Graph ClassificationGraph RegressionNode ClassificationUnderstanding the Power of Persistence Pairing via Permutation Test
Recently many efforts have been made to incorporate persistence diagrams, one of major tools in topological data analysis (TDA), into machine learning pipelines. To better understand the power and limitation of persist…
Graph ClassificationTopological Data AnalysisDirectional Graph Networks
The lack of anisotropic kernels in graph neural networks (GNNs) strongly limits their expressiveness, contributing to well-known issues such as over-smoothing. To overcome this limitation, we propose the first globally c…
Data AugmentationGraph ClassificationGraph RegressionNode ClassificationData-Driven Learning of Geometric Scattering Networks
We propose a new graph neural network (GNN) module, based on relaxations of recently proposed geometric scattering transforms, which consist of a cascade of graph wavelet filters. Our learnable geometric scattering (LEGS…
DescriptiveGraph ClassificationGraph Neural NetworkGraph Cross Networks with Vertex Infomax Pooling
We propose a novel graph cross network (GXN) to achieve comprehensive feature learning from multiple scales of a graph. Based on trainable hierarchical representations of a graph, GXN enables the interchange of intermedi…
ClassificationGeneral ClassificationGraph ClassificationUncertainty-Matching Graph Neural Networks to Defend Against Poisoning Attacks
Graph Neural Networks (GNNs), a generalization of neural networks to graph-structured data, are often implemented using message passes between entities of a graph. While GNNs are effective for node classification, link p…
Graph ClassificationLink PredictionNode ClassificationRevisiting Graph Neural Networks for Link Prediction
Graph neural networks (GNNs) have achieved great success in recent years. Three most common applications include node classification, link prediction, and graph classification. While there is rich literature on node clas…
ClassificationGraph ClassificationLink PredictionNode Classification+1Learning Graph Normalization for Graph Neural Networks
Graph Neural Networks (GNNs) have attracted considerable attention and have emerged as a new promising paradigm to process graph-structured data. GNNs are usually stacked to multiple layers and the node representations i…
Graph ClassificationGraph RegressionLink PredictionNode Classification