Papers Node Classification
“Node Classification” 태그가 달린 논문 2,077편 · 필터 해제
Can GNN be Good Adapter for LLMs?
Recently, large language models (LLMs) have demonstrated superior capabilities in understanding and zero-shot learning on textual data, promising significant advances for many text-related domains. In the graph domain, v…
Graph Neural NetworkNode ClassificationRecommendation SystemsTAG+1Endowing Pre-trained Graph Models with Provable Fairness
Pre-trained graph models (PGMs) aim to capture transferable inherent structural properties and apply them to different downstream tasks. Similar to pre-trained language models, PGMs also inherit biases from human society…
AttributeFairnessNode ClassificationLarge Language Model-driven Meta-structure Discovery in Heterogeneous Information Network
Heterogeneous information networks (HIN) have gained increasing popularity in recent years for capturing complex relations between diverse types of nodes. Meta-structures are proposed as a useful tool to identify the imp…
Language ModelingLanguage ModellingLarge Language ModelNode ClassificationAn end-to-end attention-based approach for learning on graphs
There has been a recent surge in transformer-based architectures for learning on graphs, mainly motivated by attention as an effective learning mechanism and the desire to supersede handcrafted operators characteristic o…
Graph ClassificationGraph RegressionMolecular Property PredictionNode Classification+1Class-Balanced and Reinforced Active Learning on Graphs
Graph neural networks (GNNs) have demonstrated significant success in various applications, such as node classification, link prediction, and graph classification. Active learning for GNNs aims to query the valuable samp…
Active LearningGraph ClassificationLink PredictionNode ClassificationHyperMagNet: A Magnetic Laplacian based Hypergraph Neural Network
In data science, hypergraphs are natural models for data exhibiting multi-way relations, whereas graphs only capture pairwise. Nonetheless, many proposed hypergraph neural networks effectively reduce hypergraphs to undir…
Node ClassificationCan we Soft Prompt LLMs for Graph Learning Tasks?
Graph plays an important role in representing complex relationships in real-world applications such as social networks, biological data and citation networks. In recent years, Large Language Models (LLMs) have achieved t…
Graph LearningGraph Neural NetworkLink PredictionNode ClassificationSimMLP: Training MLPs on Graphs without Supervision
Graph Neural Networks (GNNs) have demonstrated their effectiveness in various graph learning tasks, yet their reliance on neighborhood aggregation during inference poses challenges for deployment in latency-sensitive app…
Fraud DetectionGraph ClassificationGraph LearningInductive Bias+3GraSSRep: Graph-Based Self-Supervised Learning for Repeat Detection in Metagenomic Assembly
Repetitive DNA (repeats) poses significant challenges for accurate and efficient genome assembly and sequence alignment. This is particularly true for metagenomic data, where genome dynamics such as horizontal gene trans…
Node ClassificationSelf-Supervised LearningGraph-Skeleton: ~1% Nodes are Sufficient to Represent Billion-Scale Graph
Due to the ubiquity of graph data on the web, web graph mining has become a hot research spot. Nonetheless, the prevalence of large-scale web graphs in real applications poses significant challenges to storage, computati…
Feature CorrelationGraph MiningNode ClassificationLow-Rank Graph Contrastive Learning for Node Classification
Graph Neural Networks (GNNs) have been widely used to learn node representations and with outstanding performance on various tasks such as node classification. However, noise, which inevitably exists in real-world graph …
ClassificationContrastive LearningNode ClassificationTransductive LearningDisambiguated Node Classification with Graph Neural Networks
Graph Neural Networks (GNNs) have demonstrated significant success in learning from graph-structured data across various domains. Despite their great successful, one critical challenge is often overlooked by existing wor…
ClassificationContrastive LearningNode ClassificationRepresentation LearningMessage Detouring: A Simple Yet Effective Cycle Representation for Expressive Graph Learning
Graph learning is crucial in the fields of bioinformatics, social networks, and chemicals. Although high-order graphlets, such as cycles, are critical to achieving an informative graph representation for node classificat…
ClassificationGraph ClassificationGraph LearningNode ClassificationNetInfoF Framework: Measuring and Exploiting Network Usable Information
Given a node-attributed graph, and a graph task (link prediction or node classification), can we tell if a graph neural network (GNN) will perform well? More specifically, do the graph structure and the node features car…
Graph Neural NetworkLink PredictionNode ClassificationPredictionGraphTranslator: Aligning Graph Model to Large Language Model for Open-ended Tasks
Large language models (LLMs) like ChatGPT, exhibit powerful zero-shot and instruction-following capabilities, have catalyzed a revolutionary transformation across diverse fields, especially for open-ended tasks. While th…
Graph Question AnsweringInstruction FollowingLanguage ModelingLanguage Modelling+4HyperBERT: Mixing Hypergraph-Aware Layers with Language Models for Node Classification on Text-Attributed Hypergraphs
Hypergraphs are characterized by complex topological structure, representing higher-order interactions among multiple entities through hyperedges. Lately, hypergraph-based deep learning methods to learn informative data …
Inductive BiasLanguage ModelingLanguage ModellingNode ClassificationRethinking Node-wise Propagation for Large-scale Graph Learning
Scalable graph neural networks (GNNs) have emerged as a promising technique, which exhibits superior predictive performance and high running efficiency across numerous large-scale graph-based web applications. However, (…
Graph LearningNode ClassificationFlexible infinite-width graph convolutional networks and the importance of representation learning
A common theoretical approach to understanding neural networks is to take an infinite-width limit, at which point the outputs become Gaussian process (GP) distributed. This is known as a neural network Gaussian process (…
ClassificationGraph ClassificationNode ClassificationRepresentation LearningExGRG: Explicitly-Generated Relation Graph for Self-Supervised Representation Learning
Self-supervised Learning (SSL) has emerged as a powerful technique in pre-training deep learning models without relying on expensive annotated labels, instead leveraging embedded signals in unlabeled data. While SSL has …
Data AugmentationGraph GenerationGraph Representation LearningNode Classification+3Large Language Model Meets Graph Neural Network in Knowledge Distillation
In service-oriented architectures, accurately predicting the Quality of Service (QoS) is crucial for maintaining reliability and enhancing user satisfaction. However, significant challenges remain due to existing methods…
Contrastive LearningGraph AttentionGraph Neural NetworkKnowledge Distillation+5