Papers Graph Learning
“Graph Learning” 태그가 달린 논문 2,014편 · 필터 해제
Multi-View Graph Feature Propagation for Privacy Preservation and Feature Sparsity
Graph Neural Networks (GNNs) have demonstrated remarkable success in node classification tasks over relational data, yet their effectiveness often depends on the availability of complete node features. In many real-world…
Node ClassificationGraph LearningQuestion-Adaptive Graph Learning for Multi-hop Retrieval Augmented Generation
Retrieval-augmented generation (RAG) has demonstrated its ability to enhance Large Language Models (LLMs) by integrating external knowledge sources. However, multi-hop questions, which require the identification of multi…
Graph Representation LearningGraph Neural NetworkGraph LearningControllable Graph Generation with Diffusion Models via Inference-Time Tree Search Guidance
Graph generation is a fundamental problem in graph learning with broad applications across Web-scale systems, knowledge graphs, and scientific domains such as drug and material discovery. Recent approaches leverage diffu…
Graph GenerationKnowledge GraphsGraph LearningGlance for Context: Learning When to Leverage LLMs for Node-Aware GNN-LLM Fusion
Learning on text-attributed graphs has motivated the use of Large Language Models (LLMs) for graph learning. However, most fusion strategies are applied uniformly across all nodes and attain only small overall performanc…
Graph LearningMulti-View Graph Learning with Graph-Tuple
Graph Neural Networks (GNNs) typically scale with the number of graph edges, making them well suited for sparse graphs but less efficient on dense graphs, such as point clouds or molecular interactions. A common remedy i…
Molecular Property PredictionGraph LearningPoint CloudsWhat Do Temporal Graph Learning Models Learn?
Learning on temporal graphs has become a central topic in graph representation learning, with numerous benchmarks indicating the strong performance of state-of-the-art models. However, recent work has raised concerns abo…
Graph Representation LearningGraph LearningThe Unreasonable Effectiveness of Randomized Representations in Online Continual Graph Learning
Catastrophic forgetting is one of the main obstacles for Online Continual Graph Learning (OCGL), where nodes arrive one by one, distribution drifts may occur at any time and offline training on task-specific subgraphs is…
Graph LearningRevisiting Node Affinity Prediction in Temporal Graphs
Node affinity prediction is a common task that is widely used in temporal graph learning with applications in social and financial networks, recommender systems, and more. Recent works have addressed this task by adaptin…
Link Property PredictionGraph LearningFundamental Limits of Crystalline Equivariant Graph Neural Networks: A Circuit Complexity Perspective
Graph neural networks (GNNs) have become a core paradigm for learning on relational data. In materials science, equivariant GNNs (EGNNs) have emerged as a compelling backbone for crystalline-structure prediction, owing t…
Graph LearningWhen Does Global Attention Help? A Unified Empirical Study on Atomistic Graph Learning
Graph neural networks (GNNs) are widely used as surrogates for costly experiments and first-principles simulations to study the behavior of compounds at atomistic scale, and their architectural complexity is constantly i…
Graph LearningNeighborhood-Adaptive Generalized Linear Graph Embedding with Latent Pattern Mining
Graph embedding has been widely applied in areas such as network analysis, social network mining, recommendation systems, and bioinformatics. However, current graph construction methods often require the prior definition…
Recommendation SystemsGraph EmbeddingGraph LearningFractional Heat Kernel for Semi-Supervised Graph Learning with Small Training Sample Size
In this work, we introduce novel algorithms for label propagation and self-training using fractional heat kernel dynamics with a source term. We motivate the methodology through the classical correspondence of informatio…
Graph Neural NetworkGraph LearningTopInG: Topologically Interpretable Graph Learning via Persistent Rationale Filtration
Graph Neural Networks (GNNs) have shown remarkable success across various scientific fields, yet their adoption in critical decision-making is often hindered by a lack of interpretability. Recently, intrinsically interpr…
Graph LearningAdaptive Node Feature Selection For Graph Neural Networks
We propose an adaptive node feature selection approach for graph neural networks (GNNs) that identifies and removes unnecessary features during training. The ability to measure how features contribute to model output is …
Feature ImportanceGraph LearningAre LLMs Better GNN Helpers? Rethinking Robust Graph Learning under Deficiencies with Iterative Refinement
Graph Neural Networks (GNNs) are widely adopted in Web-related applications, serving as a core technique for learning from graph-structured data, such as text-attributed graphs. Yet in real-world scenarios, such graphs e…
Contrastive LearningGraph LearningOn The Expressive Power of GNN Derivatives
Despite significant advances in Graph Neural Networks (GNNs), their limited expressivity remains a fundamental challenge. Research on GNN expressivity has produced many expressive architectures, leading to architecture h…
Computational EfficiencyGraph LearningGDLNN: Marriage of Programming Language and Neural Networks for Accurate and Easy-to-Explain Graph Classification
We present GDLNN, a new graph machine learning architecture, for graph classification tasks. GDLNN combines a domain-specific programming language, called GDL, with neural networks. The main strength of GDLNN lies in its…
Graph ClassificationGraph LearningUrbanGraph: Physics-Informed Spatio-Temporal Dynamic Heterogeneous Graphs for Urban Microclimate Prediction
With rapid urbanization, predicting urban microclimates has become critical, as it affects building energy demand and public health risks. However, existing generative and homogeneous graph approaches fall short in captu…
Graph LearningMAPLE: Multi-scale Attribute-enhanced Prompt Learning for Few-shot Whole Slide Image Classification
Prompt learning has emerged as a promising paradigm for adapting pre-trained vision-language models (VLMs) to few-shot whole slide image (WSI) classification by aligning visual features with textual representations, ther…
Image ClassificationGraph LearningFederated Spatiotemporal Graph Learning for Passive Attack Detection in Smart Grids
Smart grids are exposed to passive eavesdropping, where attackers listen silently to communication links. Although no data is actively altered, such reconnaissance can reveal grid topology, consumption patterns, and oper…
Federated LearningGraph Learning