Finding Minimum-Weight Link-Disjoint Paths with a Few Common Nodes
Authors: Binglin Tao, Mingyu Xiao, Jingyang Zhao938-945
AAAI 2020 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Furthermore, experimental results on synthetic and real networks show that our algorithm is effective in practice. |
| Researcher Affiliation | Academia | School of Computer Science and Engineering University of Electronic Science and Technology of China |
| Pseudocode | Yes | Algorithm 1 Algorithm LDP |
| Open Source Code | No | The paper mentions that 'Both LDP and ILP are implemented in C/C++', but does not provide any link or explicit statement about the availability of their own source code. |
| Open Datasets | Yes | We consider two kinds of networks, P2P networks and communication networks, from Stanford Large Network Dataset Collection within Stanford Network Analysis Project (SNAP)3. |
| Dataset Splits | No | The paper describes generating random graphs and selecting real-world networks for evaluation, but it does not specify explicit training, validation, and test dataset splits in the context of model training, as it is an algorithm paper and not a machine learning paper. |
| Hardware Specification | Yes | on a PC with Intel Core i5 processor, and 4GB memory. |
| Software Dependencies | Yes | Specifically, the implementation of ILP formulation adopts the Gurobi Optimizer of version 8.1.11. |
| Experiment Setup | Yes | In this subsection, random graphs with n nodes and m links are generated from the Network X library with the link weight uniformly distributed in [1, 100]. During the experiments, we randomly generate 200 different graphs for each fixed pair of n and m, and for each graph, we randomly choose two nodes as the source and sink. |