Papers Node Property Prediction
“Node Property Prediction” 태그가 달린 논문 57편 · 필터 해제
Towards the Explainability of Temporal Graph Networks via Memory Backtracking and Topological Attribution
Temporal graphs are ubiquitous in real-world applications and Temporal Graph Networks (TGNs) have achieved superior predictive accuracy. Understanding which historical events drive model predictions can enhance trustwort…
Node Property PredictionGraph ClassificationLink PredictionA Fair Evaluation of Graph Foundation Models for Node Property Prediction
Due to the wide use of graph-structured data in different fields of industry and science, the development of Graph Foundation Models (GFMs) has recently attracted a lot of attention. While many different types of models …
Node Property PredictionRecommendation SystemsGraph Neural NetworkFraud DetectiongHAWK: Local and Global Structure Encoding for Scalable Training of Graph Neural Networks on Knowledge Graphs
Knowledge Graphs (KGs) are a rich source of structured, heterogeneous data, powering a wide range of applications. A common approach to leverage this data is to train a graph neural network (GNN) on the KG. However, exis…
Node Property PredictionGraph Neural NetworkKnowledge GraphsLink PredictionEquivariance Everywhere All At Once: A Recipe for Graph Foundation Models
Graph machine learning architectures are typically tailored to specific tasks on specific datasets, which hinders their broader applicability. This has led to a new quest in graph machine learning: how to build graph fou…
AllNode ClassificationNode Property PredictionProperty PredictionMixture of Experts Meets Decoupled Message Passing: Towards General and Adaptive Node Classification
Graph neural networks excel at graph representation learning but struggle with heterophilous data and long-range dependencies. And graph transformers address these issues through self-attention, yet face scalability and …
Computational EfficiencyGraph Representation LearningMixture-of-Experts+3Classic GNNs are Strong Baselines: Reassessing GNNs for Node Classification
Graph Transformers (GTs) have recently emerged as popular alternatives to traditional message-passing Graph Neural Networks (GNNs), due to their theoretically superior expressiveness and impressive performance reported o…
Node ClassificationNode Property PredictionEfficient Heterogeneous Graph Learning via Random Projection
Heterogeneous Graph Neural Networks (HGNNs) are powerful tools for deep learning on heterogeneous graphs. Typical HGNNs require repetitive message passing during training, limiting efficiency for large-scale real-world g…
Graph LearningGraph Neural NetworkHeterogeneous Node ClassificationNode Property PredictionHigher-order Graph Convolutional Network with Flower-Petals Laplacians on Simplicial Complexes
Despite the recent successes of vanilla Graph Neural Networks (GNNs) on various tasks, their foundation on pairwise networks inherently limits their capacity to discern latent higher-order interactions in complex systems…
Node ClassificationNode Property PredictionLong-range Meta-path Search on Large-scale Heterogeneous Graphs
Utilizing long-range dependency, a concept extensively studied in homogeneous graphs, remains underexplored in heterogeneous graphs, especially on large ones, posing two significant challenges: Reducing computational cos…
Node ClassificationNode Property PredictionTemporal Graph Benchmark for Machine Learning on Temporal Graphs
We present the Temporal Graph Benchmark (TGB), a collection of challenging and diverse benchmark datasets for realistic, reproducible, and robust evaluation of machine learning models on temporal graphs. TGB datasets are…
Node Property PredictionProperty PredictionSGFormer: Simplifying and Empowering Transformers for Large-Graph Representations
Learning representations on large-sized graphs is a long-standing challenge due to the inter-dependence nature involved in massive data points. Transformers, as an emerging class of foundation encoders for graph-structur…
Node Property PredictionPhilosophyProperty PredictionSpectral Heterogeneous Graph Convolutions via Positive Noncommutative Polynomials
Heterogeneous Graph Neural Networks (HGNNs) have gained significant popularity in various heterogeneous graph learning tasks. However, most existing HGNNs rely on spatial domain-based methods to aggregate information, i.…
Graph LearningNode ClassificationNode Property PredictionvalidHarnessing Explanations: LLM-to-LM Interpreter for Enhanced Text-Attributed Graph Representation Learning
Representation learning on text-attributed graphs (TAGs) has become a critical research problem in recent years. A typical example of a TAG is a paper citation graph, where the text of each paper serves as node attribute…
Decision MakingGeneral KnowledgeGraph Neural NetworkGraph Representation Learning+5A Comprehensive Study on Large-Scale Graph Training: Benchmarking and Rethinking
Large-scale graph training is a notoriously challenging problem for graph neural networks (GNNs). Due to the nature of evolving graph structures into the training process, vanilla GNNs usually fail to scale up, limited b…
BenchmarkingGPUNode ClassificationNode Property PredictionDay-Ahead Hourly Solar Irradiance Forecasting Based on Multi-Attributed Spatio-Temporal Graph Convolutional Network
Solar irradiance forecasting is fundamental and essential for commercializing solar energy generation by overcoming output variability. Accurate forecasting depends on historical solar irradiance data, correlations betwe…
Node Property PredictionSolar Irradiance ForecastingGPPT: Graph Pre-training and Prompt Tuning to Generalize Graph Neural Networks
Despite the promising representation learning of graph neural networks (GNNs), the supervised training of GNNs notoriously requires large amounts of labeled data from each application. An effective solution is to apply t…
Few-Shot LearningNode ClassificationNode Property PredictionRepresentation Learning+1A Gaze into the Internal Logic of Graph Neural Networks, with Logic
Graph Neural Networks share with Logic Programming several key relational inference mechanisms. The datasets on which they are trained and evaluated can be seen as database facts containing ground terms. This makes possi…
Node Property PredictionProperty PredictionSimple and Efficient Heterogeneous Graph Neural Network
Heterogeneous graph neural networks (HGNNs) have powerful capability to embed rich structural and semantic information of a heterogeneous graph into node representations. Existing HGNNs inherit many mechanisms from graph…
Graph Neural NetworkHeterogeneous Node ClassificationNode Property PredictionGraph Attention Multi-Layer Perceptron
Graph neural networks (GNNs) have achieved great success in many graph-based applications. However, the enormous size and high sparsity level of graphs hinder their applications under industrial scenarios. Although some …
Graph AttentionNode Property PredictionLabel-Enhanced Graph Neural Network for Semi-supervised Node Classification
Graph Neural Networks (GNNs) have been widely applied in the semi-supervised node classification task, where a key point lies in how to sufficiently leverage the limited but valuable label information. Most of the classi…
Graph Neural NetworkNode ClassificationNode Property Prediction