Papers Graph Property Prediction
“Graph Property Prediction” 태그가 달린 논문 57편 · 필터 해제
TGM: a Modular and Efficient Library for Machine Learning on Temporal Graphs
Well-designed open-source software drives progress in Machine Learning (ML) research. While static graph ML enjoys mature frameworks like PyTorch Geometric and DGL, ML for temporal graphs (TG), networks that evolve over …
Graph Property PredictionGraph Positional Autoencoders as Self-supervised Learners
Graph self-supervised learning seeks to learn effective graph representations without relying on labeled data. Among various approaches, graph autoencoders (GAEs) have gained significant attention for their efficiency an…
Graph Property PredictionMissing ElementsNode ClassificationProperty Prediction+2Message-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+2GotenNet: Rethinking Efficient 3D Equivariant Graph Neural Networks
Understanding complex three-dimensional (3D) structures of graphs is essential for accurately modeling various properties, yet many existing approaches struggle with fully capturing the intricate spatial relationships an…
Atomic ForcesComputational EfficiencyGraph Property PredictionGraph Regression+1Unlocking the Potential of Classic GNNs for Graph-level Tasks: Simple Architectures Meet Excellence
Message-passing Graph Neural Networks (GNNs) are often criticized for their limited expressiveness, issues like over-smoothing and over-squashing, and challenges in capturing long-range dependencies, while Graph Transfor…
Graph ClassificationGraph Property PredictionGraph RegressionNode ClassificationGraph Generative Pre-trained Transformer
Graph generation is a critical task in numerous domains, including molecular design and social network analysis, due to its ability to model complex relationships and structured data. While most modern graph generative m…
Graph GenerationGraph Property PredictionPredictionProperty PredictionVirtual Nodes Can Help: Tackling Distribution Shifts in Federated Graph Learning
Federated Graph Learning (FGL) enables multiple clients to jointly train powerful graph learning models, e.g., Graph Neural Networks (GNNs), without sharing their local graph data for graph-related downstream tasks, such…
Graph LearningGraph Property PredictionProperty PredictionData-Driven Self-Supervised Graph Representation Learning
Self-supervised graph representation learning (SSGRL) is a representation learning paradigm used to reduce or avoid manual labeling. An essential part of SSGRL is graph data augmentation. Existing methods usually rely on…
Data AugmentationDocument ClassificationGraph Property PredictionGraph Representation Learning+3Next Level Message-Passing with Hierarchical Support Graphs
Message-Passing Neural Networks (MPNNs) are extensively employed in graph learning tasks but suffer from limitations such as the restricted scope of information exchange, by being confined to neighboring nodes during eac…
Graph ClassificationGraph LearningGraph Property PredictionGraph Regression+1Towards Neural Scaling Laws for Foundation Models on Temporal Graphs
The field of temporal graph learning aims to learn from evolving network data to forecast future interactions. Given a collection of observed temporal graphs, is it possible to predict the evolution of an unseen network …
Graph LearningGraph Property PredictionProperty PredictionLearning Long Range Dependencies on Graphs via Random Walks
Message-passing graph neural networks (GNNs) excel at capturing local relationships but struggle with long-range dependencies in graphs. In contrast, graph transformers (GTs) enable global information exchange but often …
Graph ClassificationGraph Property PredictionGraph RegressionGraph Representation Learning+3Graph neural networks with configuration cross-attention for tensor compilers
With the recent popularity of neural networks comes the need for efficient serving of inference workloads. A neural network inference workload can be represented as a computational graph with nodes as operators transform…
Graph Property PredictionGraph RegressionRuntime rankingGlobal Concept Explanations for Graphs by Contrastive Learning
Beyond improving trust and validating model fairness, xAI practices also have the potential to recover valuable scientific insights in application domains where little to no prior human intuition exists. To that end, we …
Contrastive LearningFairnessGraph Property PredictionPrediction+1Contextualized Messages Boost Graph Representations
Graph neural networks (GNNs) have gained significant attention in recent years for their ability to process data that may be represented as graphs. This has prompted several studies to explore their representational capa…
Graph Property PredictionProperty PredictionTriplet Interaction Improves Graph Transformers: Accurate Molecular Graph Learning with Triplet Graph Transformers
Graph transformers typically lack third-order interactions, limiting their geometric understanding which is crucial for tasks like molecular geometry prediction. We propose the Triplet Graph Transformer (TGT) that enable…
Drug DiscoveryGraph LearningGraph Property PredictionGraph Regression+8Graph Transformers for Large Graphs
Transformers have recently emerged as powerful neural networks for graph learning, showcasing state-of-the-art performance on several graph property prediction tasks. However, these results have been limited to small-sca…
Graph LearningGraph Property PredictionNode ClassificationProperty Prediction+1Attacking Graph Neural Networks with Bit Flips: Weisfeiler and Lehman Go Indifferent
Prior attacks on graph neural networks have mostly focused on graph poisoning and evasion, neglecting the network's weights and biases. Traditional weight-based fault injection attacks, such as bit flip attacks used for …
Graph Neural NetworkGraph Property PredictionProperty PredictionTpuGraphs: A Performance Prediction Dataset on Large Tensor Computational Graphs
Precise hardware performance models play a crucial role in code optimizations. They can assist compilers in making heuristic decisions or aid autotuners in identifying the optimal configuration for a given program. For e…
Graph Property PredictionPredictionProperty PredictionRuntime rankingWeisfeiler and Lehman Go Paths: Learning Topological Features via Path Complexes
Graph Neural Networks (GNNs), despite achieving remarkable performance across different tasks, are theoretically bounded by the 1-Weisfeiler-Lehman test, resulting in limitations in terms of graph expressivity. Even thou…
Graph ClassificationGraph Property PredictionGraph RegressionEquiformerV2: Improved Equivariant Transformer for Scaling to Higher-Degree Representations
Equivariant Transformers such as Equiformer have demonstrated the efficacy of applying Transformers to the domain of 3D atomistic systems. However, they are limited to small degrees of equivariant representations due to …
Graph Property Prediction