Papers Node Classification
“Node Classification” 태그가 달린 논문 2,077편 · 필터 해제
Multi-frame Detection via Graph Neural Networks: A Link Prediction Approach
Multi-frame detection algorithms can effectively utilize the correlation between consecutive echoes to improve the detection performance of weak targets. Existing efficient multi-frame detection algorithms are typically …
Link PredictionNode ClassificationExplanation-Preserving Augmentation for Semi-Supervised Graph Representation Learning
Graph representation learning (GRL), enhanced by graph augmentation methods, has emerged as an effective technique achieving performance improvements in wide tasks such as node classification and graph classification. In…
Graph ClassificationGraph Representation LearningNode ClassificationRepresentation LearningRethinking Graph Transformer Architecture Design for Node Classification
Graph Transformer (GT), as a special type of Graph Neural Networks (GNNs), utilizes multi-head attention to facilitate high-order message passing. However, this also imposes several limitations in node classification app…
ClassificationComputational EfficiencyNode ClassificationReplay-and-Forget-Free Graph Class-Incremental Learning: A Task Profiling and Prompting Approach
Class-incremental learning (CIL) aims to continually learn a sequence of tasks, with each task consisting of a set of unique classes. Graph CIL (GCIL) follows the same setting but needs to deal with graph tasks (e.g., no…
class-incremental learningClass Incremental LearningGraph Neural NetworkIncremental Learning+2LLM-Based Multi-Agent Systems are Scalable Graph Generative Models
The structural properties of naturally arising social graphs are extensively studied to understand their evolution. Prior approaches for modeling network dynamics typically rely on rule-based models, which lack realism a…
BenchmarkingGraph GenerationNode ClassificationFedGraph: A Research Library and Benchmark for Federated Graph Learning
Federated graph learning is an emerging field with significant practical challenges. While algorithms have been proposed to improve the accuracy of training graph neural networks, such as node classification on federated…
BenchmarkingFederated LearningGraph LearningNode ClassificationDiffusing to the Top: Boost Graph Neural Networks with Minimal Hyperparameter Tuning
Graph Neural Networks (GNNs) are proficient in graph representation learning and achieve promising performance on versatile tasks such as node classification and link prediction. Usually, a comprehensive hyperparameter t…
Graph Representation LearningLink PredictionNode ClassificationRepresentation LearningImproving Node Representation by Boosting Target-Aware Contrastive Loss
Graphs model complex relationships between entities, with nodes and edges capturing intricate connections. Node representation learning involves transforming nodes into low-dimensional embeddings. These embeddings are ty…
Contrastive LearningLink PredictionNode ClassificationRepresentation Learning+1Diss-l-ECT: Dissecting Graph Data with Local Euler Characteristic Transforms
The Euler Characteristic Transform (ECT) is an efficiently-computable geometrical-topological invariant that characterizes the global shape of data. In this paper, we introduce the Local Euler Characteristic Transform ($…
Graph Representation LearningNode ClassificationRepresentation LearningClassContrast: Bridging the Spatial and Contextual Gaps for Node Representations
Graph Neural Networks (GNNs) have revolutionized the domain of graph representation learning by utilizing neighborhood aggregation schemes in many popular architectures, such as message passing graph neural networks (MPG…
Graph Representation LearningLink PredictionNode ClassificationRepresentation LearningHow to Make LLMs Strong Node Classifiers?
Language Models (LMs) are increasingly challenging the dominance of domain-specific models, such as Graph Neural Networks (GNNs) and Graph Transformers (GTs), in graph learning tasks. Following this trend, we propose a n…
Graph LearningNode ClassificationSemantic RetrievalDuoGNN: Topology-aware Graph Neural Network with Homophily and Heterophily Interaction-Decoupling
Graph Neural Networks (GNNs) have proven effective in various medical imaging applications, such as automated disease diagnosis. However, due to the local neighborhood aggregation paradigm in message passing which charac…
Graph Neural NetworkNode ClassificationConvolutional Signal Propagation: A Simple Scalable Algorithm for Hypergraphs
Last decade has seen the emergence of numerous methods for learning on graphs, particularly Graph Neural Networks (GNNs). These methods, however, are often not directly applicable to more complex structures like bipartit…
Node ClassificationRetrievalOn the Impact of Feature Heterophily on Link Prediction with Graph Neural Networks
Heterophily, or the tendency of connected nodes in networks to have different class labels or dissimilar features, has been identified as challenging for many Graph Neural Network (GNN) models. While the challenges of ap…
Graph LearningGraph Neural NetworkLink PredictionNode Classification+1Conformal Prediction: A Theoretical Note and Benchmarking Transductive Node Classification in Graphs
Conformal prediction has become increasingly popular for quantifying the uncertainty associated with machine learning models. Recent work in graph uncertainty quantification has built upon this approach for conformal gra…
BenchmarkingConformal PredictionNode ClassificationPrediction+1GISExplainer: On Explainability of Graph Neural Networks via Game-theoretic Interaction Subgraphs
Explainability is crucial for the application of black-box Graph Neural Networks (GNNs) in critical fields such as healthcare, finance, cybersecurity, and more. Various feature attribution methods, especially the perturb…
Computational EfficiencyNode ClassificationPreventing Representational Rank Collapse in MPNNs by Splitting the Computational Graph
The ability of message-passing neural networks (MPNNs) to fit complex functions over graphs is limited as most graph convolutions amplify the same signal across all feature channels, a phenomenon known as rank collapse, …
Node ClassificationHyperedge Modeling in Hypergraph Neural Networks by using Densest Overlapping Subgraphs
Hypergraphs tackle the limitations of traditional graphs by introducing {\em hyperedges}. While graph edges connect only two nodes, hyperedges connect an arbitrary number of nodes along their edges. Also, the underlying …
Graph ClusteringNode ClassificationLeiden-Fusion Partitioning Method for Effective Distributed Training of Graph Embeddings
In the area of large-scale training of graph embeddings, effective training frameworks and partitioning methods are critical for handling large networks. However, they face two major challenges: 1) existing synchronized …
Community DetectionNode ClassificationCliquePH: Higher-Order Information for Graph Neural Networks through Persistent Homology on Clique Graphs
Graph neural networks have become the default choice by practitioners for graph learning tasks such as graph classification and node classification. Nevertheless, popular graph neural network models still struggle to cap…
Graph ClassificationGraph LearningGraph Neural NetworkNode Classification+1