Papers Graph Learning
“Graph Learning” 태그가 달린 논문 2,014편 · 필터 해제
TrustGLM: Evaluating the Robustness of GraphLLMs Against Prompt, Text, and Structure Attacks
Inspired by the success of large language models (LLMs), there is a significant research shift from traditional graph learning methods to LLM-based graph frameworks, formally known as GraphLLMs. GraphLLMs leverage the re…
AttributeGraph LearningCollaborative Interest-aware Graph Learning for Group Identification
With the popularity of social media, an increasing number of users are joining group activities on online social platforms. This elicits the requirement of group identification (GI), which is to recommend groups to users…
Graph LearningSemanticST: Spatially Informed Semantic Graph Learning for Clustering, Integration, and Scalable Analysis of Spatial Transcriptomics
Spatial transcriptomics (ST) technologies enable gene expression profiling with spatial resolution, offering unprecedented insights into tissue organization and disease heterogeneity. However, current analysis methods of…
BenchmarkingContrastive Learninggraph constructionGraph Learning+1Graph-MLLM: Harnessing Multimodal Large Language Models for Multimodal Graph Learning
Multimodal Large Language Models (MLLMs) have demonstrated remarkable capabilities in representing and understanding diverse modalities. However, they typically focus on modality alignment in a pairwise manner while over…
Graph LearningIn-Context LearningRecommendation SystemsLeveraging Low-rank Factorizations of Conditional Correlation Matrices in Graph Learning
This paper addresses the problem of learning an undirected graph from data gathered at each nodes. Within the graph signal processing framework, the topology of such graph can be linked to the support of the conditional …
Graph LearningRiemannian optimizationDevil's Hand: Data Poisoning Attacks to Locally Private Graph Learning Protocols
Graph neural networks (GNNs) have achieved significant success in graph representation learning and have been applied to various domains. However, many real-world graphs contain sensitive personal information, such as us…
Data PoisoningGraph LearningGraph Representation LearningNode Classification+2Towards Multi-modal Graph Large Language Model
Multi-modal graphs, which integrate diverse multi-modal features and relations, are ubiquitous in real-world applications. However, existing multi-modal graph learning methods are typically trained from scratch for speci…
Graph LearningIn-Context LearningLanguage ModelingLanguage Modelling+2Graph Prompting for Graph Learning Models: Recent Advances and Future Directions
Graph learning models have demonstrated great prowess in learning expressive representations from large-scale graph data in a wide variety of real-world scenarios. As a prevalent strategy for training powerful graph lear…
Graph LearningHSG-12M: A Large-Scale Spatial Multigraph Dataset
Existing graph benchmarks assume non-spatial, simple edges, collapsing physically distinct paths into a single link. We introduce HSG-12M, the first large-scale dataset of $\textbf{spatial multigraphs}-$graphs embedded i…
Graph Learningscientific discoveryH$^2$GFM: Towards unifying Homogeneity and Heterogeneity on Text-Attributed Graphs
The growing interests and applications of graph learning in diverse domains have propelled the development of a unified model generalizing well across different graphs and tasks, known as the Graph Foundation Model (GFM)…
Graph LearningEviNet: Evidential Reasoning Network for Resilient Graph Learning in the Open and Noisy Environments
Graph learning has been crucial to many real-world tasks, but they are often studied with a closed-world assumption, with all possible labels of data known a priori. To enable effective graph learning in an open and nois…
Graph LearningLogical ReasoningOut-of-Distribution DetectionMasked Language Models are Good Heterogeneous Graph Generalizers
Heterogeneous graph neural networks (HGNNs) excel at capturing structural and semantic information in heterogeneous graphs (HGs), while struggling to generalize across domains and tasks. Recently, some researchers have t…
Graph LearningLanguage ModelingLanguage ModellingMasked Language ModelingNonlinear Causal Discovery for Grouped Data
Inferring cause-effect relationships from observational data has gained significant attention in recent years, but most methods are limited to scalar random variables. In many important domains, including neuroscience, p…
Causal DiscoveryGraph LearningModel SelectionGeometric Visual Fusion Graph Neural Networks for Multi-Person Human-Object Interaction Recognition in Videos
Human-Object Interaction (HOI) recognition in videos requires understanding both visual patterns and geometric relationships as they evolve over time. Visual and geometric features offer complementary strengths. Visual f…
Graph LearningGraph Neural NetworkHuman-Object Interaction DetectionSOC-DGL: Social Interaction Behavior Inspired Dual Graph Learning Framework for Drug-Target Interaction Identification
The identification of drug-target interactions (DTI) is crucial for drug discovery and repositioning, as it reveals potential uses of existing drugs, aiding in the acceleration of the drug development process and reducin…
Drug DiscoveryGraph LearningEfficient Learning of Balanced Signed Graphs via Sparse Linear Programming
Signed graphs are equipped with both positive and negative edge weights, encoding pairwise correlations as well as anti-correlations in data. A balanced signed graph is a signed graph with no cycles containing an odd num…
Graph LearningModel SelectionTopological Structure Learning Should Be A Research Priority for LLM-Based Multi-Agent Systems
Large Language Model-based Multi-Agent Systems (MASs) have emerged as a powerful paradigm for tackling complex tasks through collaborative intelligence. Nevertheless, the question of how agents should be structurally org…
Graph LearningLarge Language ModelAlgorithm Unrolling-based Denoising of Multimodal Graph Signals
We propose a denoising method of multimodal graph signals by iteratively solving signal restoration and graph learning problems. Many complex-structured data, i.e., those on sensor networks, can capture multiple modaliti…
DenoisingGraph LearningLearning Individual Behavior in Agent-Based Models with Graph Diffusion Networks
Agent-Based Models (ABMs) are powerful tools for studying emergent properties in complex systems. In ABMs, agent behaviors are governed by local interactions and stochastic rules. However, these rules are, in general, no…
Graph LearningCellCLAT: Preserving Topology and Trimming Redundancy in Self-Supervised Cellular Contrastive Learning
Self-supervised topological deep learning (TDL) represents a nascent but underexplored area with significant potential for modeling higher-order interactions in simplicial complexes and cellular complexes to derive repre…
Contrastive LearningGraph LearningMeta-LearningSelf-Supervised Learning