Papers Graph Classification
“Graph Classification” 태그가 달린 논문 1,025편 · 필터 해제
Graph Neural Tangent Kernel: Fusing Graph Neural Networks with Graph Kernels
While graph kernels (GKs) are easy to train and enjoy provable theoretical guarantees, their practical performances are limited by their expressive power, as the kernel function often depends on hand-crafted combinatoria…
Graph ClassificationStrategies for Pre-training Graph Neural Networks
Many applications of machine learning require a model to make accurate pre-dictions on test examples that are distributionally different from training ones, while task-specific labels are scarce during training. An effec…
Graph ClassificationMolecular Property PredictionOpen-Ended Question AnsweringProperty Prediction+2Solving graph compression via optimal transport
We propose a new approach to graph compression by appeal to optimal transport. The transport problem is seeded with prior information about node importance, attributes, and edges in the graph. The transport formulation c…
General ClassificationGraph ClassificationProvably Powerful Graph Networks
Recently, the Weisfeiler-Lehman (WL) graph isomorphism test was used to measure the expressive power of graph neural networks (GNN). It was shown that the popular message passing GNN cannot distinguish between graphs tha…
Graph ClassificationGraph LearningGraph RegressionGraph Filtration Learning
We propose an approach to learning with graph-structured data in the problem domain of graph classification. In particular, we present a novel type of readout operation to aggregate node features into a graph-level repre…
General ClassificationGraph ClassificationEdge Contraction Pooling for Graph Neural Networks
Graph Neural Network (GNN) research has concentrated on improving convolutional layers, with little attention paid to developing graph pooling layers. Yet pooling layers can enable GNNs to reason over abstracted groups o…
General ClassificationGraph ClassificationGraph Neural NetworkMutual Information Maximization in Graph Neural Networks
A variety of graph neural networks (GNNs) frameworks for representation learning on graphs have been recently developed. These frameworks rely on aggregation and iteration scheme to learn the representation of nodes. How…
General ClassificationGraph ClassificationGraph Neural NetworkLink Prediction+1Function Space Pooling For Graph Convolutional Networks
Convolutional layers in graph neural networks are a fundamental type of layer which output a representation or embedding of each graph vertex. The representation typically encodes information about the vertex in question…
General ClassificationGraph ClassificationIPC: A Benchmark Data Set for Learning with Graph-Structured Data
Benchmark data sets are an indispensable ingredient of the evaluation of graph-based machine learning methods. We release a new data set, compiled from International Planning Competitions (IPC), for benchmarking graph cl…
BenchmarkingGraph Classificationgraph constructionOn Graph Classification Networks, Datasets and Baselines
Graph classification receives a great deal of attention from the non-Euclidean machine learning community. Recent advances in graph coarsening have enabled the training of deeper networks and produced new state-of-the-ar…
BIG-bench Machine LearningClassificationGeneral ClassificationGraph ClassificationAre Powerful Graph Neural Nets Necessary? A Dissection on Graph Classification
Graph Neural Nets (GNNs) have received increasing attentions, partially due to their superior performance in many node and graph classification tasks. However, there is a lack of understanding on what they are learning a…
ClassificationGeneral ClassificationGraph ClassificationGraph U-Nets
We consider the problem of representation learning for graph data. Convolutional neural networks can naturally operate on images, but have significant challenges in dealing with graph data. Given images are special cases…
DecoderGeneral ClassificationGraph ClassificationGraph Embedding+2PiNet: A Permutation Invariant Graph Neural Network for Graph Classification
We propose an end-to-end deep learning learning model for graph classification and representation learning that is invariant to permutation of the nodes of the input graphs. We address the challenge of learning a fixed s…
ClassificationGeneral ClassificationGraph ClassificationGraph Neural Network+1Understanding Attention and Generalization in Graph Neural Networks
We aim to better understand attention over nodes in graph neural networks (GNNs) and identify factors influencing its effectiveness. We particularly focus on the ability of attention GNNs to generalize to larger, more co…
Graph ClassificationLearning Graph Neural Networks with Noisy Labels
We study the robustness to symmetric label noise of GNNs training procedures. By combining the nonlinear neural message-passing models (e.g. Graph Isomorphism Networks, GraphSAGE, etc.) with loss correction methods, we p…
General ClassificationGraph ClassificationGraph Transformer
Graph neural networks (GNN) have gained increasing research interests as a mean to the challenging goal of robust and universal graph learning. Previous GNNs have assumed single pre-fixed graph structure and permitted on…
Few-Shot LearningGeneral ClassificationGraph ClassificationGraph Learning+1DEEP GEOMETRICAL GRAPH CLASSIFICATION
Most of the existing Graph Neural Networks (GNNs) are the mere extension of the Convolutional Neural Networks (CNNs) to graphs. Generally, they consist of several steps of message passing between the nodes followed by a …
ClassificationClusteringGeneral ClassificationGraph Classification+2Graph Classification with Geometric Scattering
One of the most notable contributions of deep learning is the application of convolutional neural networks (ConvNets) to structured signal classification, and in particular image classification. Beyond their impressive p…
ClassificationGeneral ClassificationGraph Classificationimage-classification+1Capsule Graph Neural Network
The high-quality node embeddings learned from the Graph Neural Networks (GNNs) have been applied to a wide range of node-based applications and some of them have achieved state-of-the-art (SOTA) performance. However, whe…
Graph ClassificationGraph Neural NetworkGraph Convolutional Networks with EigenPooling
Graph neural networks, which generalize deep neural network models to graph structured data, have attracted increasing attention in recent years. They usually learn node representations by transforming, propagating and a…
ClassificationGeneral ClassificationGraph ClassificationGraph Neural Network+4