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
Towards characterizing the value of edge embeddings in Graph Neural Networks
Dhruv Rohatgi, Tanya Marwah, Zachary Chase Lipton, Jianfeng Lu, Ankur Moitra, Andrej Risteski
Scalable Weibull Graph Attention Autoencoder for Modeling Document Networks
Chaojie Wang, Xinyang Liu, Dongsheng Wang, Hao Zhang, Bo Chen, Mingyuan Zhou
LSTM-Based Proactive Congestion Management for Internet of Vehicle Networks
Aly Sabri Abdalla, Ahmad Al-Kabbany, Ehab F. Badran, Vuk Marojevic
GETS: Ensemble Temperature Scaling for Calibration in Graph Neural Networks
Dingyi Zhuang, Chonghe Jiang, Yunhan Zheng, Shenhao Wang, Jinhua Zhao
PrivQuant: Communication-Efficient Private Inference with Quantized Network/Protocol Co-Optimization
Tianshi Xu, Shuzhang Zhong, Wenxuan Zeng, Runsheng Wang, Meng Li
DAWN: Designing Distributed Agents in a Worldwide Network
Zahra Aminiranjbar, Jianan Tang, Qiudan Wang, Shubha Pant, Mahesh Viswanathan
On the impact of key design aspects in simulated Hybrid Quantum Neural Networks for Earth Observation
Lorenzo Papa, Alessandro Sebastianelli, Gabriele Meoni, Irene Amerini