Papers Graph Learning
“Graph Learning” 태그가 달린 논문 2,014편 · 필터 해제
Language Model-Enhanced Message Passing for Heterophilic Graph Learning
Traditional graph neural networks (GNNs), which rely on homophily-driven message passing, struggle with heterophilic graphs where connected nodes exhibit dissimilar features and different labels. While existing methods a…
Active LearningGraph LearningLanguage ModelingLanguage ModellingInterpretable Graph Learning Over Sets of Temporally-Sparse Data
Real-world medical data often includes measurements from multiple signals that are collected at irregular and asynchronous time intervals. For example, different types of blood tests can be measured at different times an…
Fake News DetectionGraph LearningMortality PredictionUsing Large Language Models to Tackle Fundamental Challenges in Graph Learning: A Comprehensive Survey
Graphs are a widely used paradigm for representing non-Euclidean data, with applications ranging from social network analysis to biomolecular prediction. Conventional graph learning approaches typically rely on fixed str…
Graph LearningMessage-Passing State-Space Models: Improving Graph Learning with Modern Sequence Modeling
The recent success of State-Space Models (SSMs) in sequence modeling has motivated their adaptation to graph learning, giving rise to Graph State-Space Models (GSSMs). However, existing GSSMs operate by applying SSM modu…
Computational EfficiencyGraph LearningGraph Property PredictionNode Classification+2Scalable Graph Generative Modeling via Substructure Sequences
Graph neural networks (GNNs) has been predominantly driven by message-passing, where node representations are iteratively updated via local neighborhood aggregation. Despite their success, message-passing suffers from fu…
Graph ClassificationGraph LearningNode ClassificationTransfer LearningHigher-order Structure Boosts Link Prediction on Temporal Graphs
Temporal Graph Neural Networks (TGNNs) have gained growing attention for modeling and predicting structures in temporal graphs. However, existing TGNNs primarily focus on pairwise interactions while overlooking higher-or…
Dynamic Link PredictionGraph LearningGraph Neural NetworkHypergraph representations+1Graph Foundation Models: A Comprehensive Survey
Graph-structured data pervades domains such as social networks, biological systems, knowledge graphs, and recommender systems. While foundation models have transformed natural language processing, vision, and multimodal …
Graph LearningKnowledge GraphsRecommendation SystemsSurveyTime Tracker: Mixture-of-Experts-Enhanced Foundation Time Series Forecasting Model with Decoupled Training Pipelines
In the past few years, time series foundation models have achieved superior predicting accuracy. However, real-world time series often exhibit significant diversity in their temporal patterns across different time spans …
Graph LearningMixture-of-ExpertsTime SeriesTime Series ForecastingEC-LDA : Label Distribution Inference Attack against Federated Graph Learning with Embedding Compression
Graph Neural Networks (GNNs) have been widely used for graph analysis. Federated Graph Learning (FGL) is an emerging learning framework to collaboratively train graph data from various clients. However, since clients are…
Graph LearningInference AttackLink PredictionNode ClassificationUnify Graph Learning with Text: Unleashing LLM Potentials for Session Search
Session search involves a series of interactive queries and actions to fulfill user's complex information need. Current strategies typically prioritize sequential modeling for deep semantic understanding, overlooking the…
Contrastive LearningGraph LearningLink PredictionSession SearchWhen LLMs meet open-world graph learning: a new perspective for unlabeled data uncertainty
Recently, large language models (LLMs) have significantly advanced text-attributed graph (TAG) learning. However, existing methods inadequately handle data uncertainty in open-world scenarios, especially concerning limit…
Graph LearningTAGLightweight Transformer via Unrolling of Mixed Graph Algorithms for Traffic Forecast
To forecast traffic with both spatial and temporal dimensions, we unroll a mixed-graph-based optimization algorithm into a lightweight and interpretable transformer-like neural net. Specifically, we construct two graphs:…
Graph LearningTowards Effective Federated Graph Foundation Model via Mitigating Knowledge Entanglement
Recent advances in graph machine learning have shifted to data-centric paradigms, driven by two emerging fields: (1) Federated graph learning (FGL) enables multi-client collaboration but faces challenges from data and ta…
Domain GeneralizationGraph LearningInductive BiasAdaptive Tokenization: On the Hop-Overpriority Problem in Tokenized Graph Learning Models
Graph Transformers, leveraging the global attention to capture long-range dependencies in graph structures, have significantly advanced graph machine learning, but face prohibitive computational complexity. Tokenized Gra…
Graph AttentionGraph LearningNever Skip a Batch: Continuous Training of Temporal GNNs via Adaptive Pseudo-Supervision
Temporal Graph Networks (TGNs), while being accurate, face significant training inefficiencies due to irregular supervision signals in dynamic graphs, which induce sparse gradient updates. We first theoretically establis…
Graph LearningMulti-Attribute Graph Estimation with Sparse-Group Non-Convex Penalties
We consider the problem of inferring the conditional independence graph (CIG) of high-dimensional Gaussian vectors from multi-attribute data. Most existing methods for graph estimation are based on single-attribute model…
AttributeGraph LearningRelation-Aware Graph Foundation Model
In recent years, large language models (LLMs) have demonstrated remarkable generalization capabilities across various natural language processing (NLP) tasks. Similarly, graph foundation models (GFMs) have emerged as a p…
Data AugmentationGraph LearningmodelRelation+1Urban Representation Learning for Fine-grained Economic Mapping: A Semi-supervised Graph-based Approach
Fine-grained economic mapping through urban representation learning has emerged as a crucial tool for evidence-based economic decisions. While existing methods primarily rely on supervised or unsupervised approaches, the…
Data IntegrationGraph LearningMulti-Task LearningRepresentation LearningUnfolded Deep Graph Learning for Networked Over-the-Air Computation
Over-the-air computation (AirComp) has emerged as a promising technology that enables simultaneous transmission and computation through wireless channels. In this paper, we investigate the networked AirComp in multiple c…
Graph LearningJoint Graph Estimation and Signal Restoration for Robust Federated Learning
We propose a robust aggregation method for model parameters in federated learning (FL) under noisy communications. FL is a distributed machine learning paradigm in which a central server aggregates local model parameters…
Federated LearningGraph LearningMissing Values