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This https://arxiv.org/abs/2411.06268 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_ees…

Constraints and Variables Reduction for Optimal Power Flow Using Hierarchical Graph Neural Networks with Virtual Node-Splitting
Power system networks are often modeled as homogeneous graphs, which limits the ability of graph neural network (GNN) to capture individual generator features at the same nodes. By introducing the proposed virtual node-splitting strategy, generator-level attributes like costs, limits, and ramp rates can be fully captured by GNN models, improving GNN's learning capacity and prediction accuracy. Optimal power flow (OPF) problem is used for real-time grid operations. Limited timeframe motivates st…

Brain-inspired interpretable reservoir computing with resonant recurrent neural networks
Mark A. Kramer
https://arxiv.org/abs/2506.17083 https://

Brain-inspired interpretable reservoir computing with resonant recurrent neural networks
Traditional artificial neural networks consist of nodes with non-oscillatory dynamics. Biological neural networks, on the other hand, consist of oscillatory components embedded in an oscillatory environment. Motivated by this feature of biological neurons, we describe a reservoir computing framework with explicit damped, oscillatory node dynamics. We express the oscillatory dynamics using two history dependent terms to connect these dynamics with existing artificial neural network approaches an…

Node-neighbor subnetworks and Hk-core decomposition
Dinghua Shi, Yang Zhao, Guanrong Chen
https://arxiv.org/abs/2507.04948 https://ar…

Node-neighbor subnetworks and Hk-core decomposition
The network homology Hk-core decomposition proposed in this article is similar to the k-core decomposition based on node degrees of the network. The C. elegans neural network and the cat cortical network are used as examples to reveal the symmetry of the deep structures of such networks. First, based on the concept of neighborhood in mathematics, some new concepts are introduced, including such as node-neighbor subnetwork and Betti numbers of the neighbor subnetwork, among others. Then, the Bet…

Neighborhood Overlap-Aware High-Order Graph Neural Network for Dynamic Graph Learning
Ling Wang
https://arxiv.org/abs/2506.06728 https://

Neighborhood Overlap-Aware High-Order Graph Neural Network for Dynamic Graph Learning
Dynamic graph learning (DGL) aims to learn informative and temporally-evolving node embeddings to support downstream tasks such as link prediction. A fundamental challenge in DGL lies in effectively modeling both the temporal dynamics and structural dependencies of evolving graph topologies. Recent advances in Dynamic Graph Neural Networks (DGNNs) have obtained remarkable success by leveraging message-passing mechanisms to capture pairwise node interactions. However, these approaches often over…

Equivariant Electronic Hamiltonian Prediction with Many-Body Message Passing
Chen Qian, Valdas Vitartas, James Kermode, Reinhard J. Maurer
https://arxiv.org/abs/2508.15108 https…

Equivariant Electronic Hamiltonian Prediction with Many-Body Message Passing
Machine learning surrogates of Kohn-Sham Density Functional Theory Hamiltonians offer a powerful tool to accelerate the prediction of electronic properties of materials, such as electronic band structures and densities-of-states. For large-scale applications, an ideal model would exhibit high generalization ability and computational efficiency. Here, we introduce the MACE-H graph neural network, which combines high body-order message passing with a node-order expansion to efficiently obtain all…

Predicting Flow-Induced Vibration in Isolated and Tandem Cylinders Using Hypergraph Neural Networks
Shayan Heydari, Rui Gao, Rajeev K Jaiman
https://arxiv.org/abs/2507.02242

Predicting Flow-Induced Vibration in Isolated and Tandem Cylinders Using Hypergraph Neural Networks
We present a finite element-inspired hypergraph neural network framework for predicting flow-induced vibrations in freely oscillating cylinders. The surrogate architecture transforms unstructured computational meshes into node-element hypergraphs that encode higher-order spatial relationships through element-based connectivity, preserving the geometric and topological structure of the underlying finite-element discretization. The temporal evolution of the fluid-structure interaction is modeled …

Influence Maximization in Multi-layer Social Networks Based on Differentiated Graph Embeddings
Ronghua Lin, Runbin Yao, Yijia Wang, Junjie Lin, Zhengyang Wu, Yong Tang
https://arxiv.org/abs/2508.10289 …

Influence Maximization in Multi-layer Social Networks Based on Differentiated Graph Embeddings
Identifying influential nodes is crucial in social network analysis. Existing methods often neglect local opinion leader tendencies, resulting in overlapping influence ranges for seed nodes. Furthermore, approaches based on vanilla graph neural networks (GNNs) struggle to effectively aggregate influence characteristics during message passing, particularly with varying influence intensities. Current techniques also fail to adequately address the multi-layer nature of social networks and node het…

Generating Large Semi-Synthetic Graphs of Any Size
Rodrigo Tuna, Carlos Soares
https://arxiv.org/abs/2507.02166 https://arxiv.org/pdf…

Generating Large Semi-Synthetic Graphs of Any Size
Graph generation is an important area in network science. Traditional approaches focus on replicating specific properties of real-world graphs, such as small diameters or power-law degree distributions. Recent advancements in deep learning, particularly with Graph Neural Networks, have enabled data-driven methods to learn and generate graphs without relying on predefined structural properties. Despite these advances, current models are limited by their reliance on node IDs, which restricts thei…