Results 81 to 90 of about 24,295 (253)

XGA-E: an explainability-enhanced graph neural network for network traffic anomaly detection

Cybersecurity
Graph neural network (GNN) have demonstrated excellent performance in network traffic anomaly detection research. However, existing GNN-based approaches often lack interpretability, and their detection performance remains to be improved. To address these
Min Yang, Caiming Liu
doaj   +1 more source

SGS-GNN: A Supervised Graph Sparsification method for Graph Neural Networks

CoRR
We propose SGS-GNN, a novel supervised graph sparsifier that learns the sampling probability distribution of edges and samples sparse subgraphs of a user-specified size to reduce the computational costs required by GNNs for inference tasks on large graphs.
Siddhartha Shankar Das, Naheed Anjum Arafat, Muftiqur Rahman, S. M. Ferdous, Alex Pothen, Mahantesh M. Halappanavar   +5 more
openaire   +2 more sources

Smart Exploration of Perovskite Photovoltaics: From AI Driven Discovery to Autonomous Laboratories

Advanced Energy Materials, EarlyView.
In this review, we summarize the fundamentals of AI in automated materials science, and review AI applications in perovskite solar cells. Then, we sum up recent progress in AI‐guided manufacturing optimization, and highlight AI‐driven high‐throughput and autonomous laboratories.
Wenning Chen, Zheyu Zhang, Kelvian T. Mularso, Bonghyun Jo, Hyun Suk Jung   +4 more
wiley   +1 more source

SA-GNN: Prediction of material properties using graph neural network based on multi-head self-attention optimization

AIP Advances
With the development of science and technology and the improvement of hardware computing power, the application of large models in the field of artificial intelligence (AI) has become a current research hotspot Among the focal points in the field of deep
Yasen Cui, Jian Zhu, Wei Zhou, Huaijuan Zang, Yongsheng Ren, Jiajia Xu, Shu Zhan, Wenhui Ma   +7 more
doaj   +1 more source

AST-GNN: A Graph Neural Network for Web Tracking Detection

Proceedings of the 3rd GNNet Workshop on Graph Neural Networking Workshop
Web tracking technology is prevalent on the Internet today, with most websites employing user identification systems that can accurately identify users or devices behind browsers. While numerous works in literature attempt to create machine learning models for detecting these identification systems, many rely on features susceptible to obfuscation ...
Eloi Campeny-Roig, Ismael Castell-Uroz, Pere Barlet-Ros   +2 more
openaire   +2 more sources

Machine Learning Interatomic Potentials for Energy Materials: Architectures, Training Strategies, and Applications

Advanced Energy Materials, EarlyView.
Machine learning interatomic potentials bridge quantum accuracy and computational efficiency for materials discovery. Architectures from Gaussian process regression to equivariant graph neural networks, training strategies including active learning and foundation models, and applications in solid‐state electrolytes, batteries, electrocatalysts ...
In Kee Park, Saeed Pourasad, Jinhong Mun, Arunkumar Chitteth Rajan, Tae Hyeon Park, Anand Rohit, Mohammad Zafari, Tan‐Lien Pham, Eunjae Jung, Bonseung Koo, Miran Ha, Gi Beom Sim, Hyun Gyu Park, Won Woong Choi, Kwanghyeon Jo, Soohaeng Yoo Willow, David ChangMo Yang, Chang Woo Myung, Geunsik Lee, Kwang S. Kim   +19 more
wiley   +1 more source

Unifying topological structure and self-attention mechanism for node classification in directed networks

Scientific Reports
Graph data is essential for modeling complex relationships among entities. Graph Neural Networks (GNNs) have demonstrated effectiveness in processing low-order undirected graph data; however, in complex directed graphs, relationships between nodes extend
Yue Peng, Jiwen Xia, Dafeng Liu, Miao Liu, Long Xiao, Benyun Shi   +5 more
doaj   +1 more source

A Spatio-Temporal Graph Neural Network for Predicting Flow Fields on Unstructured Grids with the SUBOFF Benchmark

Journal of Marine Science and Engineering
To overcome the limitations of traditional convolutional and recurrent neural networks in capturing spatio-temporal dynamics in flow fields on unstructured grids, this study proposes a novel Spatio-Temporal Graph Neural Network (ST-GNN) model that ...
Wei Guo, Cheng Cheng, Chong Huang, Zhiqing Lu, Kang Chen, Jun Ding   +5 more
doaj   +1 more source

Sustainable Material Cutting Optimization Using Deep Q-Networks: A Reinforcement Learning Approach for Resource Efficiency [PDF]

ITM Web of Conferences
This paper proposes an innovative approach to the Cutting Stock Problem (CSP) by integrating Graph Neural Networks (GNN) which effectively extract and process graph-structured data and Deep Reinforcement Learning (DRL) which utilizes the data generated ...
Chen Linxuan
doaj   +1 more source

AI in chemical engineering: From promise to practice

AIChE Journal, EarlyView.
Abstract Artificial intelligence (AI) in chemical engineering has moved from promise to practice: physics‐aware (gray‐box) models are gaining traction, reinforcement learning complements model predictive control (MPC), and generative AI powers documentation, digitization, and safety workflows.
Jia Wei Chew, Ronnie Andersson, Thomas Bierweiler, Patrik Ryttestal, Torsten Wik   +4 more
wiley   +1 more source