Papers Node Classification
“Node Classification” 태그가 달린 논문 2,077편 · 필터 해제
Tailoring Self-Attention for Graph via Rooted Subtrees
Attention mechanisms have made significant strides in graph learning, yet they still exhibit notable limitations: local attention faces challenges in capturing long-range information due to the inherent problems of the m…
Graph AttentionGraph LearningGraph Neural NetworkNode ClassificationLabel-free Node Classification on Graphs with Large Language Models (LLMS)
In recent years, there have been remarkable advancements in node classification achieved by Graph Neural Networks (GNNs). However, they necessitate abundant high-quality labels to ensure promising performance. In contras…
Node ClassificationGRAPES: Learning to Sample Graphs for Scalable Graph Neural Networks
Graph neural networks (GNNs) learn to represent nodes by aggregating information from their neighbors. As GNNs increase in depth, their receptive field grows exponentially, leading to high memory costs. Several existing …
Graph SamplingNode ClassificationHoloNets: Spectral Convolutions do extend to Directed Graphs
Within the graph learning community, conventional wisdom dictates that spectral convolutional networks may only be deployed on undirected graphs: Only there could the existence of a well-defined graph Fourier transform b…
Graph LearningNode ClassificationDeepHGCN: Toward Deeper Hyperbolic Graph Convolutional Networks
Hyperbolic graph convolutional networks (HGCNs) have demonstrated significant potential in extracting information from hierarchical graphs. However, existing HGCNs are limited to shallow architectures due to the computat…
Computational EfficiencyLink PredictionNode ClassificationDistilling Influences to Mitigate Prediction Churn in Graph Neural Networks
Models with similar performances exhibit significant disagreement in the predictions of individual samples, referred to as prediction churn. Our work explores this phenomenon in graph neural networks by investigating dif…
Knowledge DistillationNode ClassificationPredictionNP$^2$L: Negative Pseudo Partial Labels Extraction for Graph Neural Networks
How to utilize the pseudo labels has always been a research hotspot in machine learning. However, most methods use pseudo labels as supervised training, and lack of valid assessing for their accuracy. Moreover, applicati…
Graph LearningLink PredictionNode ClassificationvalidThe Map Equation Goes Neural: Mapping Network Flows with Graph Neural Networks
Community detection is an essential tool for unsupervised data exploration and revealing the organisational structure of networked systems. With a long history in network science, community detection typically relies on …
ClusteringCommunity DetectionGraph ClusteringGraph Neural Network+1Deep Insights into Noisy Pseudo Labeling on Graph Data
Pseudo labeling (PL) is a wide-applied strategy to enlarge the labeled dataset by self-annotating the potential samples during the training process. Several works have shown that it can improve the graph learning model p…
Graph LearningLink PredictionNode ClassificationPseudo LabelSheaf Hypergraph Networks
Higher-order relations are widespread in nature, with numerous phenomena involving complex interactions that extend beyond simple pairwise connections. As a result, advancements in higher-order processing can accelerate …
Inductive BiasNode ClassificationCan LLMs Effectively Leverage Graph Structural Information through Prompts, and Why?
Large language models (LLMs) are gaining increasing attention for their capability to process graphs with rich text attributes, especially in a zero-shot fashion. Recent studies demonstrate that LLMs obtain decent text c…
Graph LearningNode Classificationtext-classificationText Classification+1From Cluster Assumption to Graph Convolution: Graph-based Semi-Supervised Learning Revisited
Graph-based semi-supervised learning (GSSL) has long been a hot research topic. Traditional methods are generally shallow learners, based on the cluster assumption. Recently, graph convolutional networks (GCNs) have beco…
Node ClassificationHigher-order Graph Convolutional Network with Flower-Petals Laplacians on Simplicial Complexes
Despite the recent successes of vanilla Graph Neural Networks (GNNs) on various tasks, their foundation on pairwise networks inherently limits their capacity to discern latent higher-order interactions in complex systems…
Node ClassificationNode Property PredictionPrototype-Enhanced Hypergraph Learning for Heterogeneous Information Networks
The variety and complexity of relations in multimedia data lead to Heterogeneous Information Networks (HINs). Capturing the semantics from such networks requires approaches capable of utilizing the full richness of the H…
Node ClassificationArticle Classification with Graph Neural Networks and Multigraphs
Classifying research output into context-specific label taxonomies is a challenging and relevant downstream task, given the volume of existing and newly published articles. We propose a method to enhance the performance …
ArticlesClassificationGraph Neural NetworkNode ClassificationGraph Contrastive Learning Meets Graph Meta Learning: A Unified Method for Few-shot Node Tasks
Graph Neural Networks (GNNs) have become popular in Graph Representation Learning (GRL). One fundamental application is few-shot node classification. Most existing methods follow the meta learning paradigm, showing the a…
CoLAContrastive LearningGraph Representation LearningMeta-Learning+2Crypto'Graph: Leveraging Privacy-Preserving Distributed Link Prediction for Robust Graph Learning
Graphs are a widely used data structure for collecting and analyzing relational data. However, when the graph structure is distributed across several parties, its analysis is particularly challenging. In particular, due …
Graph LearningGraph Neural NetworkLink PredictionNode Classification+1Semi-supervised Domain Adaptation on Graphs with Contrastive Learning and Minimax Entropy
Label scarcity in a graph is frequently encountered in real-world applications due to the high cost of data labeling. To this end, semi-supervised domain adaptation (SSDA) on graphs aims to leverage the knowledge of a la…
Contrastive LearningDomain AdaptationNode ClassificationSemi-supervised Domain AdaptationBregman Graph Neural Network
Numerous recent research on graph neural networks (GNNs) has focused on formulating GNN architectures as an optimization problem with the smoothness assumption. However, in node classification tasks, the smoothing effect…
Bilevel OptimizationGraph Neural NetworkNode ClassificationUniKG: A Benchmark and Universal Embedding for Large-Scale Knowledge Graphs
Irregular data in real-world are usually organized as heterogeneous graphs (HGs) consisting of multiple types of nodes and edges. To explore useful knowledge from real-world data, both the large-scale encyclopedic HG dat…
AttributeGraph LearningGraph Representation LearningKnowledge Graphs+2