Method of Maximum Two-Commodity Flow Search in a Fuzzy Temporal Graph

2017 ◽  
pp. 249-260 ◽  
Author(s):  
Alexander Bozhenyuk ◽  
Evgeniya Gerasimenko ◽  
Igor Rozenberg
Keyword(s):  
Commodity Flow ◽  
Temporal Graph

scholarly journals Adaptation of Random Binomial Graphs for Testing Network Flow Problems Algorithms

Mathematics ◽  
2021 ◽  
Vol 9 (15) ◽  
pp. 1716
Author(s):  
Adrian Marius Deaconu ◽  
Delia Spridon
Keyword(s):  
Network Flow ◽  
Large Scale ◽  
Random Network ◽  
Minimum Cost ◽  
Maximum Flow ◽  
Network Flow Problems ◽  
Commodity Flow ◽  
Vast Number ◽  
Flow Problems ◽  
Execution Speed

Algorithms for network flow problems, such as maximum flow, minimum cost flow, and multi-commodity flow problems, are continuously developed and improved, and so, random network generators become indispensable to simulate the functionality and to test the correctness and the execution speed of these algorithms. For this purpose, in this paper, the well-known Erdős–Rényi model is adapted to generate random flow (transportation) networks. The developed algorithm is fast and based on the natural property of the flow that can be decomposed into directed elementary s-t paths and cycles. So, the proposed algorithm can be used to quickly build a vast number of networks as well as large-scale networks especially designed for s-t flows.

scholarly journals Cyber—Physical Attack Detection in Water Distribution Systems with Temporal Graph Convolutional Neural Networks

Water ◽  
2021 ◽  
Vol 13 (9) ◽  
pp. 1247
Author(s):  
Lydia Tsiami ◽  
Christos Makropoulos
Keyword(s):  
Neural Networks ◽  
Distribution System ◽  
Distribution Systems ◽  
Water Distribution ◽  
Attack Detection ◽  
Convolutional Network ◽  
Irreversible Damage ◽  
Physical Attack ◽  
Temporal Graph ◽  
Graph Neural Networks

Prompt detection of cyber–physical attacks (CPAs) on a water distribution system (WDS) is critical to avoid irreversible damage to the network infrastructure and disruption of water services. However, the complex interdependencies of the water network’s components make CPA detection challenging. To better capture the spatiotemporal dimensions of these interdependencies, we represented the WDS as a mathematical graph and approached the problem by utilizing graph neural networks. We presented an online, one-stage, prediction-based algorithm that implements the temporal graph convolutional network and makes use of the Mahalanobis distance. The algorithm exhibited strong detection performance and was capable of localizing the targeted network components for several benchmark attacks. We suggested that an important property of the proposed algorithm was its explainability, which allowed the extraction of useful information about how the model works and as such it is a step towards the creation of trustworthy AI algorithms for water applications. Additional insights into metrics commonly used to rank algorithm performance were also presented and discussed.

Multi-stage attention spatial-temporal graph networks for traffic prediction

2021 ◽  
Vol 428 ◽  
pp. 42-53
Author(s):  
Xueyan Yin ◽  
Genze Wu ◽  
Jinze Wei ◽  
Yanming Shen ◽  
Heng Qi ◽  
...  
Keyword(s):  
Traffic Prediction ◽  
Multi Stage ◽  
Temporal Graph

scholarly journals A Multi-Commodity Flow Problem for Fair Resource Allocation in Multi-Path Video Delivery Networks

2020 ◽  
Vol 53 (2) ◽  
pp. 7386-7391
Author(s):  
Luca De Cicco ◽  
Gioacchino Manfredi ◽  
Vittorio Palmisano ◽  
Saverio Mascolo
Keyword(s):  
Resource Allocation ◽  
Flow Problem ◽  
Video Delivery ◽  
Commodity Flow ◽  
Delivery Networks
Sign in / Sign up

Export Citation Format

Share Document