# A wave delay neural network for solving label-constrained shortest route query on time-varying communication networks

## TL;DR

This paper introduces a new neural network framework to efficiently find shortest routes in time-varying communication networks with label constraints.

## Contribution

The novel wave delay neural network (WDNN) framework is introduced for solving label-constrained routing queries with improved accuracy and speed.

## Key findings

- The WDNN framework accurately models time-varying networks without requiring training.
- The proposed algorithm outperforms existing methods in response speed and computational accuracy.
- Detailed analysis confirms the correctness and efficiency of the WDNN approach.

## Abstract

The focus of the research is on the label-constrained time-varying shortest route query problem on time-varying communication networks. To the best of our knowledge, research on this issue is still relatively limited, and similar studies have the drawbacks of low solution accuracy and slow computational speed. In this study, a wave delay neural network (WDNN) framework and corresponding algorithms is proposed to effectively solve the label-constrained time-varying shortest routing query problem. This framework accurately simulates the time-varying characteristics of the network without any training requirements. WDNN adopts a new type of wave neuron, which is independently designed and all neurons are parallelly computed on WDNN. This algorithm determines the shortest route based on the waves received by the destination neuron (node). Furthermore, the time complexity and correctness of the proposed algorithm were analyzed in detail in this study, and the performance of the algorithm was analyzed in depth by comparing it with existing algorithms on randomly generated and real networks. The research results indicate that the proposed algorithm outperforms current existing algorithms in terms of response speed and computational accuracy.