Papers Graph Classification
“Graph Classification” 태그가 달린 논문 1,025편 · 필터 해제
On the Initialization of Graph Neural Networks
Graph Neural Networks (GNNs) have displayed considerable promise in graph representation learning across various applications. The core learning process requires the initialization of model weight matrices within each GN…
Graph ClassificationGraph Representation LearningLink PredictionNode Classification+1Recurrent Distance Filtering for Graph Representation Learning
Graph neural networks based on iterative one-hop message passing have been shown to struggle in harnessing the information from distant nodes effectively. Conversely, graph transformers allow each node to attend to all o…
Graph ClassificationGraph Representation LearningInductive BiasLong-range modeling+2On the Adversarial Robustness of Graph Contrastive Learning Methods
Contrastive learning (CL) has emerged as a powerful framework for learning representations of images and text in a self-supervised manner while enhancing model robustness against adversarial attacks. More recently, resea…
Adversarial RobustnessContrastive LearningGraph ClassificationNode ClassificationDisentangling the Spectral Properties of the Hodge Laplacian: Not All Small Eigenvalues Are Equal
The rich spectral information of the graph Laplacian has been instrumental in graph theory, machine learning, and graph signal processing for applications such as graph classification, clustering, or eigenmode analysis. …
AllClusteringGraph ClassificationHard Label Black Box Node Injection Attack on Graph Neural Networks
While graph neural networks have achieved state-of-the-art performances in many real-world tasks including graph classification and node classification, recent works have demonstrated they are also extremely vulnerable t…
ClassificationGraph ClassificationNode ClassificationBenchmarking Toxic Molecule Classification using Graph Neural Networks and Few Shot Learning
Traditional methods like Graph Convolutional Networks (GCNs) face challenges with limited data and class imbalance, leading to suboptimal performance in graph classification tasks during toxicity prediction of molecules …
BenchmarkingDrug DiscoveryFew-Shot LearningGraph ClassificationExploring Graph Classification Techniques Under Low Data Constraints: A Comprehensive Study
This survey paper presents a brief overview of recent research on graph data augmentation and few-shot learning. It covers various techniques for graph data augmentation, including node and edge perturbation, graph coars…
Data AugmentationFew-Shot LearningGraph ClassificationGraph Generation+2Going beyond persistent homology using persistent homology
Representational limits of message-passing graph neural networks (MP-GNNs), e.g., in terms of the Weisfeiler-Leman (WL) test for isomorphism, are well understood. Augmenting these graph models with topological features v…
Graph ClassificationGRAM: An Interpretable Approach for Graph Anomaly Detection using Gradient Attention Maps
Detecting unusual patterns in graph data is a crucial task in data mining. However, existing methods face challenges in consistently achieving satisfactory performance and often lack interpretability, which hinders our u…
Anomaly DetectionDecision MakingGraph Anomaly DetectionGraph ClassificationArchitecture Matters: Uncovering Implicit Mechanisms in Graph Contrastive Learning
With the prosperity of contrastive learning for visual representation learning (VCL), it is also adapted to the graph domain and yields promising performance. However, through a systematic study of various graph contrast…
Contrastive LearningGraph ClassificationGraph LearningInductive Bias+2Diversified Node Sampling based Hierarchical Transformer Pooling for Graph Representation Learning
Graph pooling methods have been widely used on downsampling graphs, achieving impressive results on multiple graph-level tasks like graph classification and graph generation. An important line called node dropping poolin…
Graph ClassificationGraph GenerationGraph Representation LearningRepresentation LearningLaplacian Canonization: A Minimalist Approach to Sign and Basis Invariant Spectral Embedding
Spectral embedding is a powerful graph embedding technique that has received a lot of attention recently due to its effectiveness on Graph Transformers. However, from a theoretical perspective, the universal expressive p…
Graph ClassificationGraph EmbeddingGraph RegressionBLIS-Net: Classifying and Analyzing Signals on Graphs
Graph neural networks (GNNs) have emerged as a powerful tool for tasks such as node classification and graph classification. However, much less work has been done on signal classification, where the data consists of many…
ClassificationGraph ClassificationNode ClassificationPSP: Pre-Training and Structure Prompt Tuning for Graph Neural Networks
Graph Neural Networks (GNNs) are powerful in learning semantics of graph data. Recently, a new paradigm "pre-train and prompt" has shown promising results in adapting GNNs to various tasks with less supervised data. The …
Contrastive LearningGraph ClassificationNode ClassificationA Causal Disentangled Multi-Granularity Graph Classification Method
Graph data widely exists in real life, with large amounts of data and complex structures. It is necessary to map graph data to low-dimensional embedding. Graph classification, a critical graph task, mainly relies on iden…
ClassificationDisentanglementGraph ClassificationGraph Representation Learning+1Generative and Contrastive Paradigms Are Complementary for Graph Self-Supervised Learning
For graph self-supervised learning (GSSL), masked autoencoder (MAE) follows the generative paradigm and learns to reconstruct masked graph edges or node features. Contrastive Learning (CL) maximizes the similarity betwee…
Contrastive LearningGraph ClassificationLink PredictionNode Classification+2Self-supervision meets kernel graph neural models: From architecture to augmentations
Graph representation learning has now become the de facto standard when handling graph-structured data, with the framework of message-passing graph neural networks (MPNN) being the most prevailing algorithmic tool. Despi…
Data AugmentationGraph ClassificationGraph Representation LearningRepresentation Learning+1Mirage: Model-Agnostic Graph Distillation for Graph Classification
GNNs, like other deep learning models, are data and computation hungry. There is a pressing need to scale training of GNNs on large datasets to enable their usage on low-resource environments. Graph distillation is an ef…
BenchmarkingClassificationData CompressionGraph Classification+1Taking the human out of decomposition-based optimization via artificial intelligence: Part I. Learning when to decompose
In this paper, we propose a graph classification approach for automatically determining whether to use a monolithic or a decomposition-based solution method. In this approach, an optimization problem is represented as a …
Graph ClassificationEnhanced Graph Neural Networks with Ego-Centric Spectral Subgraph Embeddings Augmentation
Graph Neural Networks (GNNs) have shown remarkable merit in performing various learning-based tasks in complex networks. The superior performance of GNNs often correlates with the availability and quality of node-level f…
Graph ClassificationGraph EmbeddingNode Classification