Papers Graph Learning
“Graph Learning” 태그가 달린 논문 2,014편 · 필터 해제
Reliable and Compact Graph Fine-tuning via GraphSparse Prompting
Recently, graph prompt learning has garnered increasing attention in adapting pre-trained GNN models for downstream graph learning tasks. However, existing works generally conduct prompting over all graph elements (e.g.,…
AttributeGraph LearningPrompt LearningFedSSP: Federated Graph Learning with Spectral Knowledge and Personalized Preference
Personalized Federated Graph Learning (pFGL) facilitates the decentralized training of Graph Neural Networks (GNNs) without compromising privacy while accommodating personalized requirements for non-IID participants. In …
Graph LearningA Survey of Deep Graph Learning under Distribution Shifts: from Graph Out-of-Distribution Generalization to Adaptation
Distribution shifts on graphs -- the discrepancies in data distribution between training and employing a graph machine learning model -- are ubiquitous and often unavoidable in real-world scenarios. These shifts may seve…
Graph LearningOut-of-Distribution GeneralizationMissNODAG: Differentiable Cyclic Causal Graph Learning from Incomplete Data
Causal discovery in real-world systems, such as biological networks, is often complicated by feedback loops and incomplete data. Standard algorithms, which assume acyclic structures or fully observed data, struggle with …
Causal DiscoveryGraph LearningMissing ValuesHomomorphism Counts as Structural Encodings for Graph Learning
Graph Transformers are popular neural networks that extend the well-known Transformer architecture to the graph domain. These architectures operate by applying self-attention on graph nodes and incorporating graph struct…
Graph LearningMolecular Property PredictionProperty PredictionBenchmarking Graph Learning for Drug-Drug Interaction Prediction
Predicting drug-drug interaction (DDI) plays an important role in pharmacology and healthcare for identifying potential adverse interactions and beneficial combination therapies between drug pairs. Recently, a flurry of …
BenchmarkingGraph LearningPredictionCan Self Supervision Rejuvenate Similarity-Based Link Prediction?
Although recent advancements in end-to-end learning-based link prediction (LP) methods have shown remarkable capabilities, the significance of traditional similarity-based LP methods persists in unsupervised scenarios wh…
Data AugmentationGraph LearningLink PredictionPrediction+1Perturbation-based Graph Active Learning for Weakly-Supervised Belief Representation Learning
This paper addresses the problem of optimizing the allocation of labeling resources for semi-supervised belief representation learning in social networks. The objective is to strategically identify valuable messages on s…
Active LearningData AugmentationGraph LearningRepresentation LearningMitigating Graph Covariate Shift via Score-based Out-of-distribution Augmentation
Distribution shifts between training and testing datasets significantly impair the model performance on graph learning. A commonly-taken causal view in graph invariant learning suggests that stable predictive features of…
Graph GenerationGraph LearningDisenGCD: A Meta Multigraph-assisted Disentangled Graph Learning Framework for Cognitive Diagnosis
Existing graph learning-based cognitive diagnosis (CD) methods have made relatively good results, but their student, exercise, and concept representations are learned and exchanged in an implicit unified graph, which mak…
cognitive diagnosisDiagnosticGraph AttentionGraph LearningTheoretical Insights into Line Graph Transformation on Graph Learning
Line graph transformation has been widely studied in graph theory, where each node in a line graph corresponds to an edge in the original graph. This has inspired a series of graph neural networks (GNNs) applied to trans…
Graph LearningGraph Representation LearningRepresentation LearningTAGExplainer: Narrating Graph Explanations for Text-Attributed Graph Learning Models
Representation learning of Text-Attributed Graphs (TAGs) has garnered significant attention due to its applications in various domains, including recommendation systems and social networks. Despite advancements in TAG le…
Decision MakingGraph LearningLanguage ModelingLanguage Modelling+4LangGFM: A Large Language Model Alone Can be a Powerful Graph Foundation Model
Graph foundation models (GFMs) have recently gained significant attention. However, the unique data processing and evaluation setups employed by different studies hinder a deeper understanding of their progress. Addition…
DiversityGraph LearningLanguage ModelingLanguage Modelling+3Learning Graph Quantized Tokenizers
Transformers serve as the backbone architectures of Foundational Models, where domain-specific tokenizers allow them to adapt to various domains. Graph Transformers (GTs) have recently emerged as leading models in geomet…
Graph LearningQuantizationSelf-Supervised LearningConformal Prediction for Federated Graph Neural Networks with Missing Neighbor Information
Graphs play a crucial role in data mining and machine learning, representing real-world objects and interactions. As graph datasets grow, managing large, decentralized subgraphs becomes essential, particularly within fed…
Conformal PredictionFederated LearningGraph LearningUncertainty QuantificationNT-LLM: A Novel Node Tokenizer for Integrating Graph Structure into Large Language Models
Graphs are a fundamental data structure for representing relationships in real-world scenarios. With the success of Large Language Models (LLMs) across various natural language processing (NLP) tasks, there has been grow…
Graph LearningGraph Masked Autoencoder for Spatio-Temporal Graph Learning
Effective spatio-temporal prediction frameworks play a crucial role in urban sensing applications, including traffic analysis, human mobility behavior modeling, and citywide crime prediction. However, the presence of dat…
Crime PredictionData AugmentationGraph LearningSelf-Supervised LearningOnline Multi-modal Root Cause Analysis
Root Cause Analysis (RCA) is essential for pinpointing the root causes of failures in microservice systems. Traditional data-driven RCA methods are typically limited to offline applications due to high computational dema…
Graph LearningTowards Stable, Globally Expressive Graph Representations with Laplacian Eigenvectors
Graph neural networks (GNNs) have achieved remarkable success in a variety of machine learning tasks over graph data. Existing GNNs usually rely on message passing, i.e., computing node representations by gathering infor…
Graph LearningDeeper Insights into Deep Graph Convolutional Networks: Stability and Generalization
Graph convolutional networks (GCNs) have emerged as powerful models for graph learning tasks, exhibiting promising performance in various domains. While their empirical success is evident, there is a growing need to unde…
Graph Learning