Network Programming
Network programming focuses on designing and implementing algorithms and architectures for processing information across interconnected nodes, aiming to efficiently solve complex computational problems. Current research emphasizes developing novel network architectures, such as graph neural networks and deep operator networks, and improving existing algorithms through techniques like frequency domain inference and tensor decomposition for faster and more accurate computations. These advancements are significant for diverse applications, including improved recommendation systems, enhanced anomaly detection in network flows, and more accurate causal inference from network data. The field's impact spans various scientific disciplines and practical applications, driving progress in areas like machine learning, signal processing, and social network analysis.
Papers
Cross-Attention Graph Neural Networks for Inferring Gene Regulatory Networks with Skewed Degree Distribution
Jiaqi Xiong, Nan Yin, Shiyang Liang, Haoyang Li, Yingxu Wang, Duo Ai, Fang Pan, Jingjie Wang
Adaptive Calibration: A Unified Conversion Framework of Spiking Neural Network
Ziqing Wang, Yuetong Fang, Jiahang Cao, Hongwei Ren, Renjing Xu
A Theoretical Analysis of Soft-Label vs Hard-Label Training in Neural Networks
Saptarshi Mandal, Xiaojun Lin, R. Srikant
DISHONEST: Dissecting misInformation Spread using Homogeneous sOcial NEtworks and Semantic Topic classification
Caleb Stam, Emily Saldanha, Mahantesh Halappanavar, Anurag Acharya
Learning and Current Prediction of PMSM Drive via Differential Neural Networks
Wenjie Mei, Xiaorui Wang, Yanrong Lu, Ke Yu, Shihua Li
Physics-Based Dynamic Models Hybridisation Using Physics-Informed Neural Networks
Branislava Lalic, Dinh Viet Cuong, Mina Petric, Vladimir Pavlovic, Ana Firanj Sremac, Mark Roantree
Score-matching-based Structure Learning for Temporal Data on Networks
Hao Chen, Kai Yi, Lin Liu, Yu Guang Wang
AHSG: Adversarial Attacks on High-level Semantics in Graph Neural Networks
Kai Yuan, Xiaobing Pei, Haoran Yang
Temporal-Aware Evaluation and Learning for Temporal Graph Neural Networks
Junwei Su, Shan Wu
Modeling High-Resolution Spatio-Temporal Wind with Deep Echo State Networks and Stochastic Partial Differential Equations
Kesen Wang, Minwoo Kim, Stefano Castruccio, Marc G. Genton
A Consolidated Volatility Prediction with Back Propagation Neural Network and Genetic Algorithm
Zong Ke, Jingyu Xu, Zizhou Zhang, Yu Cheng, Wenjun Wu
Covered Forest: Fine-grained generalization analysis of graph neural networks
Antonis Vasileiou, Ben Finkelshtein, Floris Geerts, Ron Levie, Christopher Morris
Fine-grained graph representation learning for heterogeneous mobile networks with attentive fusion and contrastive learning
Shengheng Liu, Tianqi Zhang, Ningning Fu, Yongming Huang