Papers Graph Classification
“Graph Classification” 태그가 달린 논문 1,025편 · 필터 해제
Graph Neural Networks for Small Graph and Giant Network Representation Learning: An Overview
Graph neural networks denote a group of neural network models introduced for the representation learning tasks on graph data specifically. Graph neural networks have been demonstrated to be effective for capturing networ…
Graph ClassificationGraph Neural NetworkRepresentation LearningInfoGraph: Unsupervised and Semi-supervised Graph-Level Representation Learning via Mutual Information Maximization
This paper studies learning the representations of whole graphs in both unsupervised and semi-supervised scenarios. Graph-level representations are critical in a variety of real-world applications such as predicting the …
Graph ClassificationMolecular Property PredictionProperty PredictionRepresentation LearningIsoNN: Isomorphic Neural Network for Graph Representation Learning and Classification
Deep learning models have achieved huge success in numerous fields, such as computer vision and natural language processing. However, unlike such fields, it is hard to apply traditional deep learning models on the graph …
ClassificationGeneral ClassificationGraph ClassificationGraph Matching+2Topology Based Scalable Graph Kernels
We propose a new graph kernel for graph classification and comparison using Ollivier Ricci curvature. The Ricci curvature of an edge in a graph describes the connectivity in the local neighborhood. An edge in a densely c…
Graph Classificationk-hop Graph Neural Networks
Graph neural networks (GNNs) have emerged recently as a powerful architecture for learning node and graph representations. Standard GNNs have the same expressive power as the Weisfeiler-Leman test of graph isomorphism in…
General ClassificationGraph ClassificationNode ClassificationSemi-Supervised Graph Embedding for Multi-Label Graph Node Classification
The graph convolution network (GCN) is a widely-used facility to realize graph-based semi-supervised learning, which usually integrates node features and graph topologic information to build learning models. However, as …
ClassificationGeneral ClassificationGraph ClassificationGraph Embedding+4Label-Aware Graph Convolutional Networks
Recent advances in Graph Convolutional Networks (GCNs) have led to state-of-the-art performance on various graph-related tasks. However, most existing GCN models do not explicitly identify whether all the aggregated neig…
ClassificationGeneral ClassificationGraph ClassificationNode Classification+1Graph Representation Learning via Hard and Channel-Wise Attention Networks
Attention operators have been widely applied in various fields, including computer vision, natural language processing, and network embedding learning. Attention operators on graph data enables learnable weights when agg…
Graph AttentionGraph ClassificationGraph EmbeddingGraph Representation Learning+3Improving Attention Mechanism in Graph Neural Networks via Cardinality Preservation
Graph Neural Networks (GNNs) are powerful to learn the representation of graph-structured data. Most of the GNNs use the message-passing scheme, where the embedding of a node is iteratively updated by aggregating the inf…
Graph ClassificationGraph Representation LearningNode ClassificationiPool -- Information-based Pooling in Hierarchical Graph Neural Networks
With the advent of data science, the analysis of network or graph data has become a very timely research problem. A variety of recent works have been proposed to generalize neural networks to graphs, either from a spectr…
Graph ClassificationSpectral Clustering with Graph Neural Networks for Graph Pooling
Spectral clustering (SC) is a popular clustering technique to find strongly connected communities on a graph. SC can be used in Graph Neural Networks (GNNs) to implement pooling operations that aggregate nodes belonging …
ClusteringGraph ClassificationGraph ClusteringGraph Neural Network+1Graph Star Net for Generalized Multi-Task Learning
In this work, we present graph star net (GraphStar), a novel and unified graph neural net architecture which utilizes message-passing relay and attention mechanism for multiple prediction tasks - node classification, gra…
ClassificationGeneral ClassificationGraph ClassificationLink Prediction+6Attacking Graph Convolutional Networks via Rewiring
Graph Neural Networks (GNNs) have boosted the performance of many graph related tasks such as node classification and graph classification. Recent researches show that graph neural networks are vulnerable to adversarial …
General ClassificationGraph ClassificationNode ClassificationReinforcement LearningA Persistent Weisfeiler–Lehman Procedure for Graph Classification
The Weisfeiler–Lehman graph kernel exhibits competitive performance in many graph classification tasks. However, its subtree features are not able to capture connected components and cycles, topological features known fo…
ClassificationGeneral ClassificationGraph ClassificationTopological Data AnalysisLabeled Graph Generative Adversarial Networks
As a new approach to train generative models, \emph{generative adversarial networks} (GANs) have achieved considerable success in image generation. This framework has also recently been applied to data with graph structu…
Graph ClassificationImage GenerationGOT: An Optimal Transport framework for Graph comparison
We present a novel framework based on optimal transport for the challenging problem of comparing graphs. Specifically, we exploit the probabilistic distribution of smooth graph signals defined with respect to the graph t…
Graph ClassificationDEMO-Net: Degree-specific Graph Neural Networks for Node and Graph Classification
Graph data widely exist in many high-impact applications. Inspired by the success of deep learning in grid-structured data, graph neural network models have been proposed to learn powerful node-level or graph-level repre…
General ClassificationGraph ClassificationGraph Neural NetworkMulti-Task Learning+2Wasserstein Weisfeiler-Lehman Graph Kernels
Most graph kernels are an instance of the class of $\mathcal{R}$-Convolution kernels, which measure the similarity of objects by comparing their substructures. Despite their empirical success, most graph kernels use a na…
Graph ClassificationExploiting Edge Features for Graph Neural Networks
Edge features contain important information about graphs. However, current state-of-the-art neural network models designed for graph learning, e.g., graph convolutional networks (GCN) and graph attention networks (GAT),…
General ClassificationGraph AttentionGraph ClassificationGraph Learning+2Discriminative structural graph classification
This paper focuses on the discrimination capacity of aggregation functions: these are the permutation invariant functions used by graph neural networks to combine the features of nodes. Realizing that the most powerful a…
ClassificationGeneral ClassificationGraph ClassificationGraph Neural Network