Abstract: With the advent of Network Function Virtualization (NFV), Physical Network Functions (PNFs) are gradually being replaced by Virtual Network Functions (VNFs) that are hosted on general ...

Leveraging ML models to directly learn network flow configurations from empirical data can deliver robustly high performance, surpassing that of demand-prediction methods. To gain a deeper theoretical ...

Abstract: Loopwise route representation (LRR), which has been recently proposed as an alternative network representation, can determine the optimal path for the vehicle routing problems with a simpler ...

Computer scientists have written a network flow algorithm that computes almost as fast as is mathematically possible. This algorithm computes the maximum traffic flow with minimum transport costs for ...

Computer scientists at ETH Zurich have written a network flow algorithm that computes almost as fast as is mathematically possible. This algorithm computes the maximum traffic flow with minimum ...

In 2022, a team of computer scientists presented a groundbreaking algorithm for the maximum flow problem: How does one transport the most supplies from a source node to a sink node in a network while ...

Betweenness centrality is one of the key measures of the node importance in a network. However, it is computationally intractable to calculate the exact betweenness centrality of nodes in large-scale ...

This data report collected photovoltaic patents under the Industry-University-Research (IUR) collaborative innovation perspective. The evolution of the collaborative innovation network from 2000 to ...

Through a vast network of nerve fibres, electrical signals are constantly travelling across the brain. This complicated activity is what ultimately gives rise to our thoughts, emotions and behaviours ...

In algorithms, as in life, negativity can be a drag. Consider the problem of finding the shortest path between two points on a graph — a network of nodes connected by links, or edges. Often, these ...