Papers Graph Classification
“Graph Classification” 태그가 달린 논문 1,025편 · 필터 해제
A Novel Higher-order Weisfeiler-Lehman Graph Convolution
Current GNN architectures use a vertex neighborhood aggregation scheme, which limits their discriminative power to that of the 1-dimensional Weisfeiler-Lehman (WL) graph isomorphism test. Here, we propose a novel graph c…
Graph ClassificationStructural Landmarking and Interaction Modelling: on Resolution Dilemmas in Graph Classification
Graph neural networks are promising architecture for learning and inference with graph-structured data. Yet difficulties in modelling the ``parts'' and their ``interactions'' still persist in terms of graph classificatio…
General ClassificationGraph ClassificationGraph Representation LearningRepresentation LearningPath Integral Based Convolution and Pooling for Graph Neural Networks
Graph neural networks (GNNs) extends the functionality of traditional neural networks to graph-structured data. Similar to CNNs, an optimized design of graph convolution and pooling is key to success. Borrowing ideas fro…
Graph ClassificationGraph RegressionregressionGraph Neural Networks in TensorFlow and Keras with Spektral
In this paper we present Spektral, an open-source Python library for building graph neural networks with TensorFlow and the Keras application programming interface. Spektral implements a large set of methods for deep lea…
Deep LearningGeneral ClassificationGraph ClassificationGraph Regression+2Graph Backdoor
One intriguing property of deep neural networks (DNNs) is their inherent vulnerability to backdoor attacks -- a trojan model responds to trigger-embedded inputs in a highly predictable manner while functioning normally o…
Backdoor AttackDescriptiveGeneral ClassificationGraph Classification+1TreeRNN: Topology-Preserving Deep GraphEmbedding and Learning
General graphs are difficult for learning due to their irregular structures. Existing works employ message passing along graph edges to extract local patterns using customized graph kernels, but few of them are effective…
Graph ClassificationGraph EmbeddingBackdoor Attacks to Graph Neural Networks
In this work, we propose the first backdoor attack to graph neural networks (GNN). Specifically, we propose a \emph{subgraph based backdoor attack} to GNN for graph classification. In our backdoor attack, a GNN classifie…
Backdoor AttackGeneral ClassificationGraph ClassificationNode ClassificationGraph Pooling with Node Proximity for Hierarchical Representation Learning
Graph neural networks have attracted wide attentions to enable representation learning of graph data in recent works. In complement to graph convolution operators, graph pooling is crucial for extracting hierarchical rep…
Graph ClassificationRepresentation LearningGCC: Graph Contrastive Coding for Graph Neural Network Pre-Training
Graph representation learning has emerged as a powerful technique for addressing real-world problems. Various downstream graph learning tasks have benefited from its recent developments, such as node classification, simi…
Contrastive LearningGeneral ClassificationGraph ClassificationGraph Learning+5Improving Graph Neural Network Expressivity via Subgraph Isomorphism Counting
While Graph Neural Networks (GNNs) have achieved remarkable results in a variety of applications, recent studies exposed important shortcomings in their ability to capture the structure of the underlying graph. It has be…
Graph ClassificationGraph Neural NetworkGraph RegressionWasserstein Embedding for Graph Learning
We present Wasserstein Embedding for Graph Learning (WEGL), a novel and fast framework for embedding entire graphs in a vector space, in which various machine learning models are applicable for graph-level prediction tas…
Computational EfficiencyGraph ClassificationGraph EmbeddingGraph Learning+2Contrastive Multi-View Representation Learning on Graphs
We introduce a self-supervised approach for learning node and graph level representations by contrasting structural views of graphs. We show that unlike visual representation learning, increasing the number of views to m…
General ClassificationGraph ClassificationLinear evaluationRepresentation Learning+1Graph-Aware Transformer: Is Attention All Graphs Need?
Graphs are the natural data structure to represent relational and structural information in many domains. To cover the broad range of graph-data applications including graph classification as well as graph generation, it…
AllDecoderGraph ClassificationGraph GenerationUnsupervised Graph Representation by Periphery and Hierarchical Information Maximization
Deep representation learning on non-Euclidean data types, such as graphs, has gained significant attention in recent years. Invent of graph neural networks has improved the state-of-the-art for both node and the entire g…
Graph ClassificationGraph Neural NetworkRepresentation LearningLittle Ball of Fur: A Python Library for Graph Sampling
Sampling graphs is an important task in data mining. In this paper, we describe Little Ball of Fur a Python library that includes more than twenty graph sampling algorithms. Our goal is to make node, edge, and exploratio…
Graph ClassificationGraph EmbeddingGraph SamplingNode ClassificationSimPool: Towards Topology Based Graph Pooling with Structural Similarity Features
Deep learning methods for graphs have seen rapid progress in recent years with much focus awarded to generalising Convolutional Neural Networks (CNN) to graph data. CNNs are typically realised by alternating convolutiona…
Graph ClassificationGraph Neural NetworkAdversarial Attack on Hierarchical Graph Pooling Neural Networks
Recent years have witnessed the emergence and development of graph neural networks (GNNs), which have been shown as a powerful approach for graph representation learning in many tasks, such as node classification and gra…
Adversarial AttackClassificationGeneral ClassificationGraph Classification+3Customized Graph Neural Networks
Recently, Graph Neural Networks (GNNs) have greatly advanced the task of graph classification. Typically, we first build a unified GNN model with graphs in a given training set and then use this unified model to predict …
ClassificationGeneral ClassificationGraph ClassificationGraph Neural NetworkCharacteristic Functions on Graphs: Birds of a Feather, from Statistical Descriptors to Parametric Models
In this paper, we propose a flexible notion of characteristic functions defined on graph vertices to describe the distribution of vertex features at multiple scales. We introduce FEATHER, a computationally efficient algo…
Graph ClassificationNode ClassificationTransfer LearningHow hard is to distinguish graphs with graph neural networks?
A hallmark of graph neural networks is their ability to distinguish the isomorphism class of their inputs. This study derives hardness results for the classification variant of graph isomorphism in the message-passing mo…
Graph Classification