Papers Graph Regression
“Graph Regression” 태그가 달린 논문 155편 · 필터 해제
TANGNN: a Concise, Scalable and Effective Graph Neural Networks with Top-m Attention Mechanism for Graph Representation Learning
In the field of deep learning, Graph Neural Networks (GNNs) and Graph Transformer models, with their outstanding performance and flexible architectural designs, have become leading technologies for processing structured …
Computational EfficiencyGraph Neural NetworkGraph RegressionGraph Representation Learning+3From Primes to Paths: Enabling Fast Multi-Relational Graph Analysis
Multi-relational networks capture intricate relationships in data and have diverse applications across fields such as biomedical, financial, and social sciences. As networks derived from increasingly large datasets becom…
Graph RegressionHeterogeneous Node ClassificationNode ClassificationRelation PredictionNode Regression on Latent Position Random Graphs via Local Averaging
Node regression consists in predicting the value of a graph label at a node, given observations at the other nodes. To gain some insight into the performance of various estimators for this task, we perform a theoretical …
Graph RegressionNode RegressionPositionregressionFIT-GNN: Faster Inference Time for GNNs Using Coarsening
Scalability of Graph Neural Networks (GNNs) remains a significant challenge, particularly when dealing with large-scale graphs. To tackle this, coarsening-based methods are used to reduce the graph into a smaller graph, …
ClassificationGraph ClassificationGraph RegressionNode Classification+2GINTRIP: Interpretable Temporal Graph Regression using Information bottleneck and Prototype-based method
Deep neural networks (DNNs) have demonstrated remarkable performance across various domains, yet their application to temporal graph regression tasks faces significant challenges regarding interpretability. This critical…
Graph RegressionMulti-Task LearningregressionHyperAggregation: Aggregating over Graph Edges with Hypernetworks
HyperAggregation is a hypernetwork-based aggregation function for Graph Neural Networks. It uses a hypernetwork to dynamically generate weights in the size of the current neighborhood, which are then used to aggregate th…
Graph ClassificationGraph RegressionNode ClassificationKAGNNs: Kolmogorov-Arnold Networks meet Graph Learning
In recent years, Graph Neural Networks (GNNs) have become the de facto tool for learning node and graph representations. Most GNNs typically consist of a sequence of neighborhood aggregation (a.k.a., message-passing) lay…
Graph ClassificationGraph LearningGraph RegressionKolmogorov-Arnold Networks+2Next 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+1A Comprehensive Graph Pooling Benchmark: Effectiveness, Robustness and Generalizability
Graph pooling has gained attention for its ability to obtain effective node and graph representations for various downstream tasks. Despite the recent surge in graph pooling approaches, there is a lack of standardized ex…
Graph ClassificationGraph RegressionNode ClassificationPANDA: Expanded Width-Aware Message Passing Beyond Rewiring
Recent research in the field of graph neural network (GNN) has identified a critical issue known as "over-squashing," resulting from the bottleneck phenomenon in graph structures, which impedes the propagation of long-ra…
Graph ClassificationGraph Neural NetworkGraph RegressionNode ClassificationLearning 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+3Spatio-Spectral Graph Neural Networks
Spatial Message Passing Graph Neural Networks (MPGNNs) are widely used for learning on graph-structured data. However, key limitations of l-step MPGNNs are that their "receptive field" is typically limited to the l-hop n…
GPUGraph ClassificationGraph Neural NetworkGraph Regression+2A Canonicalization Perspective on Invariant and Equivariant Learning
In many applications, we desire neural networks to exhibit invariance or equivariance to certain groups due to symmetries inherent in the data. Recently, frame-averaging methods emerged to be a unified framework for atta…
Graph ClassificationGraph EmbeddingGraph RegressionGraph 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 rankingConditional Local Feature Encoding for Graph Neural Networks
Graph neural networks (GNNs) have shown great success in learning from graph-based data. The key mechanism of current GNNs is message passing, where a node's feature is updated based on the information passing from its l…
Graph ClassificationGraph RegressionLink PredictionNode ClassificationCKGConv: General Graph Convolution with Continuous Kernels
The existing definitions of graph convolution, either from spatial or spectral perspectives, are inflexible and not unified. Defining a general convolution operator in the graph domain is challenging due to the lack of c…
Graph ClassificationGraph LearningGraph RegressionNode ClassificationAdvancing Graph Neural Networks with HL-HGAT: A Hodge-Laplacian and Attention Mechanism Approach for Heterogeneous Graph-Structured Data
Graph neural networks (GNNs) have proven effective in capturing relationships among nodes in a graph. This study introduces a novel perspective by considering a graph as a simplicial complex, encompassing nodes, edges, t…
Graph AttentionGraph RegressionAn end-to-end attention-based approach for learning on graphs
There has been a recent surge in transformer-based architectures for learning on graphs, mainly motivated by attention as an effective learning mechanism and the desire to supersede handcrafted operators characteristic o…
Graph ClassificationGraph RegressionMolecular Property PredictionNode Classification+1Triplet 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+8Gaussian process regression with Sliced Wasserstein Weisfeiler-Lehman graph kernels
Supervised learning has recently garnered significant attention in the field of computational physics due to its ability to effectively extract complex patterns for tasks like solving partial differential equations, or p…
Graph ClassificationGraph Regressionregression