The field of graph neural networks (GNNs) is rapidly advancing, with a focus on improving performance, efficiency, and adaptability. Recent developments have centered around addressing limitations such as over-smoothing, over-squashing, and heterophily. Novel approaches, including graph rewiring, attention mechanisms, and spectral convolutional neural networks, have been proposed to enhance the representation capacity of GNNs. Furthermore, techniques like dynamic quantization, parameter-free message passing, and local virtual nodes have been introduced to improve the efficiency and scalability of GNNs. Noteworthy papers in this area include 'A Node-Aware Dynamic Quantization Approach for Graph Collaborative Filtering', which achieves state-of-the-art performance in collaborative filtering tasks, and 'Limits of message passing for node classification', which provides a unifying statistical framework for understanding the limitations of message passing neural networks. Additionally, 'Training Transformers for Mesh-Based Simulations' demonstrates the effectiveness of graph transformers in simulating complex physical systems, while 'GegenNet' proposes a novel spectral convolutional neural network model for link sign prediction in signed bipartite graphs.
Advancements in Graph Neural Networks and Related Techniques
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A Node-Aware Dynamic Quantization Approach for Graph Collaborative Filtering
A Novel Unified Extended Matrix for Graph Signal Processing: Theory and Application
DR-CircuitGNN: Training Acceleration of Heterogeneous Circuit Graph Neural Network on GPUs
Understanding and Tackling Over-Dilution in Graph Neural Networks
Scaling Graph Transformers: A Comparative Study of Sparse and Dense Attention
Gumbel-MPNN: Graph Rewiring with Gumbel-Softmax
Limits of message passing for node classification: How class-bottlenecks restrict signal-to-noise ratio
Training Transformers for Mesh-Based Simulations
A Note on Graphon-Signal Analysis of Graph Neural Networks
Deep Learning-Enabled Supercritical Flame Simulation at Detailed Chemistry and Real-Fluid Accuracy Towards Trillion-Cell Scale
Dynamic Triangulation-Based Graph Rewiring for Graph Neural Networks
Ab-initio Quantum Transport with the GW Approximation, 42,240 Atoms, and Sustained Exascale Performance
Memorization in Graph Neural Networks
InfraredGP: Efficient Graph Partitioning via Spectral Graph Neural Networks with Negative Corrections
Parameter-Free Structural-Diversity Message Passing for Graph Neural Networks
GegenNet: Spectral Convolutional Neural Networks for Link Sign Prediction in Signed Bipartite Graphs
Multi-View Graph Convolution Network for Internal Talent Recommendation Based on Enterprise Emails
pdGRASS: A Fast Parallel Density-Aware Algorithm for Graph Spectral Sparsification
Local Virtual Nodes for Alleviating Over-Squashing in Graph Neural Networks