Papers Graph Classification
“Graph Classification” 태그가 달린 논문 1,025편 · 필터 해제
CGMN: A Contrastive Graph Matching Network for Self-Supervised Graph Similarity Learning
Graph similarity learning refers to calculating the similarity score between two graphs, which is required in many realistic applications, such as visual tracking, graph classification, and collaborative filtering. As mo…
Collaborative FilteringGraph ClassificationGraph MatchingGraph Similarity+2Recipe for a General, Powerful, Scalable Graph Transformer
We propose a recipe on how to build a general, powerful, scalable (GPS) graph Transformer with linear complexity and state-of-the-art results on a diverse set of benchmarks. Graph Transformers (GTs) have gained popularit…
Graph ClassificationGraph Property PredictionGraph RegressionGraph Representation Learning+4Compressing Deep Graph Neural Networks via Adversarial Knowledge Distillation
Deep graph neural networks (GNNs) have been shown to be expressive for modeling graph-structured data. Nevertheless, the over-stacked architecture of deep graph models makes it difficult to deploy and rapidly test on mob…
Graph ClassificationKnowledge DistillationNode ClassificationHigh-Order Pooling for Graph Neural Networks with Tensor Decomposition
Graph Neural Networks (GNNs) are attracting growing attention due to their effectiveness and flexibility in modeling a variety of graph-structured data. Exiting GNN architectures usually adopt simple pooling operations (…
ClassificationGraph ClassificationGraph Neural NetworkNode Classification+2Asynchronous Neural Networks for Learning in Graphs
This paper studies asynchronous message passing (AMP), a new paradigm for applying neural network based learning to graphs. Existing graph neural networks use the synchronous distributed computing model and aggregate the…
Distributed ComputingGraph ClassificationGraphMAE: Self-Supervised Masked Graph Autoencoders
Self-supervised learning (SSL) has been extensively explored in recent years. Particularly, generative SSL has seen emerging success in natural language processing and other AI fields, such as the wide adoption of BERT a…
Contrastive LearningGraph ClassificationGraph LearningNode Classification+2KGNN: Harnessing Kernel-based Networks for Semi-supervised Graph Classification
This paper studies semi-supervised graph classification, which is an important problem with various applications in social network analysis and bioinformatics. This problem is typically solved by using graph neural netwo…
Graph ClassificationGraph Neural NetworkTowards Explanation for Unsupervised Graph-Level Representation Learning
Due to the superior performance of Graph Neural Networks (GNNs) in various domains, there is an increasing interest in the GNN explanation problem "\emph{which fraction of the input graph is the most crucial to decide th…
Decision MakingGraph ClassificationNode ClassificationRepresentation LearningParallel and Distributed Graph Neural Networks: An In-Depth Concurrency Analysis
Graph neural networks (GNNs) are among the most powerful tools in deep learning. They routinely solve complex problems on unstructured networks, such as node classification, graph classification, or link prediction, with…
Graph ClassificationLink PredictionNode ClassificationLabel-invariant Augmentation for Semi-Supervised Graph Classification
Recently, contrastiveness-based augmentation surges a new climax in the computer vision domain, where some operations, including rotation, crop, and flip, combined with dedicated algorithms, dramatically increase the mod…
ClassificationContrastive LearningGraph ClassificationGraph Neural NetworkRepresentation Power of Graph Neural Networks: Improved Expressivity via Algebraic Analysis
Despite the remarkable success of Graph Neural Networks (GNNs), the common belief is that their representation power is limited and that they are at most as expressive as the Weisfeiler-Lehman (WL) algorithm. In this pap…
Graph ClassificationGraphHD: Efficient graph classification using hyperdimensional computing
Hyperdimensional Computing (HDC) developed by Kanerva is a computational model for machine learning inspired by neuroscience. HDC exploits characteristics of biological neural systems such as high-dimensionality, randomn…
ClassificationGraph ClassificationGraph LearningOrthogonal Gromov-Wasserstein Discrepancy with Efficient Lower Bound
Comparing structured data from possibly different metric-measure spaces is a fundamental task in machine learning, with applications in, e.g., graph classification. The Gromov-Wasserstein (GW) discrepancy formulates a co…
Graph ClassificationSpatial-temporal associations representation and application for process monitoring using graph convolution neural network
Thank you very much for the attention and concern of colleagues and scholars in this work. With the comments and guidance of experts, editors, and reviewers, this work has been accepted for publishing in the journal "Pro…
Chemical ProcessGraph ClassificationNDGGNET-A Node Independent Gate based Graph Neural Networks
Graph Neural Networks (GNNs) is an architecture for structural data, and has been adopted in a mass of tasks and achieved fabulous results, such as link prediction, node classification, graph classification and so on. Ge…
Graph ClassificationLink PredictionNode ClassificationData-Free Adversarial Knowledge Distillation for Graph Neural Networks
Graph neural networks (GNNs) have been widely used in modeling graph structured data, owing to its impressive performance in a wide range of practical applications. Recently, knowledge distillation (KD) for GNNs has enab…
Generative Adversarial NetworkGraph ClassificationKnowledge DistillationModel Compression+1Bandits for Structure Perturbation-based Black-box Attacks to Graph Neural Networks with Theoretical Guarantees
Graph neural networks (GNNs) have achieved state-of-the-art performance in many graph-based tasks such as node classification and graph classification. However, many recent works have demonstrated that an attacker can mi…
Graph ClassificationNode ClassificationClustered Graph Matching for Label Recovery and Graph Classification
Given a collection of vertex-aligned networks and an additional label-shuffled network, we propose procedures for leveraging the signal in the vertex-aligned collection to recover the labels of the shuffled network. We c…
ClassificationGraph ClassificationGraph MatchingSpiking Graph Convolutional Networks
Graph Convolutional Networks (GCNs) achieve an impressive performance due to the remarkable representation ability in learning the graph information. However, GCNs, when implemented on a deep network, require expensive c…
Graph ClassificationRecommendation SystemsFAITH: Few-Shot Graph Classification with Hierarchical Task Graphs
Few-shot graph classification aims at predicting classes for graphs, given limited labeled graphs for each class. To tackle the bottleneck of label scarcity, recent works propose to incorporate few-shot learning framewor…
ClassificationFew-Shot LearningGraph Classification