Papers Graph Learning
“Graph Learning” 태그가 달린 논문 2,014편 · 필터 해제
Exploring Graph Tasks with Pure LLMs: A Comprehensive Benchmark and Investigation
Graph-structured data has become increasingly prevalent across various domains, raising the demand for effective models to handle graph tasks like node classification and link prediction. Traditional graph learning model…
BenchmarkingGraph LearningLink PredictionNode ClassificationTask Graph Maximum Likelihood Estimation for Procedural Activity Understanding in Egocentric Videos
We introduce a gradient-based approach for learning task graphs from procedural activities, improving over hand-crafted methods. Our method directly optimizes edge weights via maximum likelihood, enabling integration int…
Graph LearningMistake DetectionOnline Mistake DetectionTask Graph Learning+1Decoupled Graph Energy-based Model for Node Out-of-Distribution Detection on Heterophilic Graphs
Despite extensive research efforts focused on OOD detection on images, OOD detection on nodes in graph learning remains underexplored. The dependence among graph nodes hinders the trivial adaptation of existing approache…
Graph LearningOut-of-Distribution DetectionExPath: Towards Explaining Targeted Pathways for Biological Knowledge Bases
Biological knowledge bases provide systemically functional pathways of cells or organisms in terms of molecular interaction. However, recognizing more targeted pathways, particularly when incorporating wet-lab experiment…
Graph LearningMambaProtein Language ModelGraph Masked Language Models
Language Models (LMs) and Graph Neural Networks (GNNs) have shown great promise in their respective areas, yet integrating structured graph data with rich textual information remains challenging. In this work, we propose…
Graph LearningKnowledge GraphsNode ClassificationA Survey of Graph Transformers: Architectures, Theories and Applications
Graph Transformers (GTs) have demonstrated a strong capability in modeling graph structures by addressing the intrinsic limitations of graph neural networks (GNNs), such as over-smoothing and over-squashing. Recent studi…
Graph LearningSurveyPosition: Graph Learning Will Lose Relevance Due To Poor Benchmarks
While machine learning on graphs has demonstrated promise in drug design and molecular property prediction, significant benchmarking challenges hinder its further progress and relevance. Current benchmarking practices of…
BenchmarkingCombinatorial OptimizationDrug DesignGraph Learning+3Adaptive Sparsified Graph Learning Framework for Vessel Behavior Anomalies
Graph neural networks have emerged as a powerful tool for learning spatiotemporal interactions. However, conventional approaches often rely on predefined graphs, which may obscure the precise relationships being modeled.…
Anomaly DetectionGraph LearningGiGL: Large-Scale Graph Neural Networks at Snapchat
Recent advances in graph machine learning (ML) with the introduction of Graph Neural Networks (GNNs) have led to a widespread interest in applying these approaches to business applications at scale. GNNs enable different…
Graph LearningDemocratizing Large Language Model-Based Graph Data Augmentation via Latent Knowledge Graphs
Data augmentation is necessary for graph representation learning due to the scarcity and noise present in graph data. Most of the existing augmentation methods overlook the context information inherited from the dataset …
Data AugmentationGraph LearningGraph Representation LearningKnowledge Graphs+4Are Large Language Models In-Context Graph Learners?
Large language models (LLMs) have demonstrated remarkable in-context reasoning capabilities across a wide range of tasks, particularly with unstructured inputs such as language or images. However, LLMs struggle to handle…
Graph LearningIn-Context LearningRAGRetrieval-augmented GenerationDual-level Mixup for Graph Few-shot Learning with Fewer Tasks
Graph neural networks have been demonstrated as a powerful paradigm for effectively learning graph-structured data on the web and mining content from it.Current leading graph models require a large number of labeled samp…
Few-Shot LearningGraph LearningMeta-LearningIncomplete Graph Learning: A Comprehensive Survey
Graph learning is a prevalent field that operates on ubiquitous graph data. Effective graph learning methods can extract valuable information from graphs. However, these methods are non-robust and affected by missing att…
AttributeGraph LearningSurveyUtilizing Effective Dynamic Graph Learning to Shield Financial Stability from Risk Propagation
Financial risks can propagate across both tightly coupled temporal and spatial dimensions, posing significant threats to financial stability. Moreover, risks embedded in unlabeled data are often difficult to detect. To a…
Graph LearningKnowledge-aware contrastive heterogeneous molecular graph learning
Molecular representation learning is pivotal in predicting molecular properties and advancing drug design. Traditional methodologies, which predominantly rely on homogeneous graph encoding, are limited by their inability…
BenchmarkingContrastive LearningDrug DesignGraph Learning+5Model Generalization on Text Attribute Graphs: Principles with Large Language Models
Large language models (LLMs) have recently been introduced to graph learning, aiming to extend their zero-shot generalization success to tasks where labeled graph data is scarce. Among these applications, inference over …
AttributeGraph LearningTAGZero-shot GeneralizationRaising the Bar in Graph OOD Generalization: Invariant Learning Beyond Explicit Environment Modeling
Out-of-distribution (OOD) generalization has emerged as a critical challenge in graph learning, as real-world graph data often exhibit diverse and shifting environments that traditional models fail to generalize across. …
Graph LearningOn Vanishing Gradients, Over-Smoothing, and Over-Squashing in GNNs: Bridging Recurrent and Graph Learning
Graph Neural Networks (GNNs) are models that leverage the graph structure to transmit information between nodes, typically through the message-passing operation. While widely successful, this approach is well known to su…
Graph LearningGraph Neural NetworkRecent Advances in Malware Detection: Graph Learning and Explainability
The rapid evolution of malware has necessitated the development of sophisticated detection methods that go beyond traditional signature-based approaches. Graph learning techniques have emerged as powerful tools for model…
Feature EngineeringGraph EmbeddingGraph LearningMalware Analysis+2Evaluating and Improving Graph-based Explanation Methods for Multi-Agent Coordination
Graph Neural Networks (GNNs), developed by the graph learning community, have been adopted and shown to be highly effective in multi-robot and multi-agent learning. Inspired by this successful cross-pollination, we inves…
Graph Learning