Notice: The reproducibility variables underlying each score are classified using an automated LLM-based pipeline, validated against a manually labeled dataset. LLM-based classification introduces uncertainty and potential bias; scores should be interpreted as estimates. Full accuracy metrics and methodology are described in Coakley et alK. L. Coakley, T. Snelleman, H. Hoos, and O. E. Gundersen, "The embrace of open science: An analysis of a decade of AI research and 56 800 conference papers," Under Review, 2026..
Finding Minimum-Weight Link-Disjoint Paths with a Few Common Nodes
Authors: Binglin Tao, Mingyu Xiao, Jingyang Zhao938-945
AAAI 2020 | Venue PDF | 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. |