Papers Graph Classification
“Graph Classification” 태그가 달린 논문 1,025편 · 필터 해제
DBGDGM: Dynamic Brain Graph Deep Generative Model
Graphs are a natural representation of brain activity derived from functional magnetic imaging (fMRI) data. It is well known that clusters of anatomical brain regions, known as functional connectivity networks (FCNs), en…
Dynamic Link PredictionFunctional ConnectivityGraph ClassificationGraph Generation+2Weakly Supervised Joint Whole-Slide Segmentation and Classification in Prostate Cancer
The segmentation and automatic identification of histological regions of diagnostic interest offer a valuable aid to pathologists. However, segmentation methods are hampered by the difficulty of obtaining pixel-level ann…
ClassificationDiagnosticGraph ClassificationNode Classification+3GANExplainer: GAN-based Graph Neural Networks Explainer
With the rapid deployment of graph neural networks (GNNs) based techniques into a wide range of applications such as link prediction, node classification, and graph classification the explainability of GNNs has become an…
Decision MakingGenerative Adversarial NetworkGraph ClassificationGraph Neural Network+2A Generalization of ViT/MLP-Mixer to Graphs
Graph Neural Networks (GNNs) have shown great potential in the field of graph representation learning. Standard GNNs define a local message-passing mechanism which propagates information over the whole graph domain by st…
Graph ClassificationGraph RegressionGraph Representation LearningRepresentation LearningRegularized Optimal Transport Layers for Generalized Global Pooling Operations
Global pooling is one of the most significant operations in many machine learning models and tasks, which works for information fusion and structured data (like sets and graphs) representation. However, without solid mat…
Graph Classificationimage-classificationImage ClassificationExploring Fake News Detection with Heterogeneous Social Media Context Graphs
Fake news detection has become a research area that goes way beyond a purely academic interest as it has direct implications on our society as a whole. Recent advances have primarily focused on textbased approaches. Howe…
ArticlesFake News DetectionGraph ClassificationGraph Neural NetworkGraph Learning for Anomaly Analytics: Algorithms, Applications, and Challenges
Anomaly analytics is a popular and vital task in various research contexts, which has been studied for several decades. At the same time, deep learning has shown its capacity in solving many graph-based tasks like, node …
Graph AttentionGraph ClassificationGraph LearningLink Prediction+1QESK: Quantum-based Entropic Subtree Kernels for Graph Classification
In this paper, we propose a novel graph kernel, namely the Quantum-based Entropic Subtree Kernel (QESK), for Graph Classification. To this end, we commence by computing the Average Mixing Matrix (AMM) of the Continuous-t…
ClassificationGraph ClassificationTotal Variation Graph Neural Networks
Recently proposed Graph Neural Networks (GNNs) for vertex clustering are trained with an unsupervised minimum cut objective, approximated by a Spectral Clustering (SC) relaxation. However, the SC relaxation is loose and,…
ClusteringGraph ClassificationGraph Contrastive Learning with Implicit Augmentations
Existing graph contrastive learning methods rely on augmentation techniques based on random perturbations (e.g., randomly adding or dropping edges and nodes). Nevertheless, altering certain edges or nodes can unexpectedl…
Contrastive LearningGraph ClassificationNode ClassificationApplication of Graph Neural Networks and graph descriptors for graph classification
Graph classification is an important area in both modern research and industry. Multiple applications, especially in chemistry and novel drug discovery, encourage rapid development of machine learning models in this area…
ClassificationDrug DiscoveryExperimental DesignGraph Classification+3Unlearning Graph Classifiers with Limited Data Resources
As the demand for user privacy grows, controlled data removal (machine unlearning) is becoming an important feature of machine learning models for data-sensitive Web applications such as social networks and recommender s…
Graph ClassificationMachine UnlearningRecommendation SystemsHAQJSK: Hierarchical-Aligned Quantum Jensen-Shannon Kernels for Graph Classification
In this work, we propose a family of novel quantum kernels, namely the Hierarchical Aligned Quantum Jensen-Shannon Kernels (HAQJSK), for un-attributed graphs. Different from most existing classical graph kernels, the pro…
Graph ClassificationUnleashing the Power of Graph Data Augmentation on Covariate Distribution Shift
The issue of distribution shifts is emerging as a critical concern in graph representation learning. From the perspective of invariant learning and stable learning, a recently well-established paradigm for out-of-distrib…
Data AugmentationGraph ClassificationGraph Representation LearningOut-of-Distribution Generalization+1Weisfeiler and Leman go Hyperbolic: Learning Distance Preserving Node Representations
In recent years, graph neural networks (GNNs) have emerged as a promising tool for solving machine learning problems on graphs. Most GNNs are members of the family of message passing neural networks (MPNNs). There is a c…
Graph ClassificationPAGE: Prototype-Based Model-Level Explanations for Graph Neural Networks
Aside from graph neural networks (GNNs) attracting significant attention as a powerful framework revolutionizing graph representation learning, there has been an increasing demand for explaining GNN models. Although vari…
Computational EfficiencyGraph ClassificationGraph Representation LearningRepresentation LearningImproving Graph Neural Networks with Learnable Propagation Operators
Graph Neural Networks (GNNs) are limited in their propagation operators. In many cases, these operators often contain non-negative elements only and are shared across channels, limiting the expressiveness of GNNs. Moreov…
Graph ClassificationGraph Fuzzy System: Concepts, Models and Algorithms
Fuzzy systems (FSs) have enjoyed wide applications in various fields, including pattern recognition, intelligent control, data mining and bioinformatics, which is attributed to the strong interpretation and learning abil…
Graph ClassificationGraph ClusteringGraph Neural NetworkGraphMAD: Graph Mixup for Data Augmentation using Data-Driven Convex Clustering
We develop a novel data-driven nonlinear mixup mechanism for graph data augmentation and present different mixup functions for sample pairs and their labels. Mixup is a data augmentation method to create new training dat…
ClusteringData AugmentationGraph ClassificationBeyond Homophily with Graph Echo State Networks
Graph Echo State Networks (GESN) have already demonstrated their efficacy and efficiency in graph classification tasks. However, semi-supervised node classification brought out the problem of over-smoothing in end-to-end…
ClassificationGraph ClassificationNode Classification