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 [1].
Path Evaluation and Centralities in Weighted Graphs - An Axiomatic Approach
Authors: Jadwiga Sosnowska, Oskar Skibski
IJCAI 2018 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Finally, in the experimental section, we compare the three classes of path evaluation functions on a random graphs constructed from 2-dimensional grid. Then, we apply newly defined centrality measures to the graph of a public transport in Warsaw. |
| Researcher Affiliation | Academia | Jadwiga Sosnowska, Oskar Skibski University of Warsaw, Poland EMAIL |
| Pseudocode | No | The paper does not contain any structured pseudocode or algorithm blocks. |
| Open Source Code | No | The paper does not provide concrete access to source code for the methodology described. |
| Open Datasets | No | To visualize the difference of three classes of PEFs, we present their performance on random graphs on grids. Specifically, we generated 75 random graphs based on 2-dimensional grids [0, 80] [0, 80]... As a sample application of centrality measures for weighted graphs, we have considered the graph of public transport of Warsaw, Poland. |
| Dataset Splits | No | The paper mentions generating random graphs and using a public transport graph, and selects a percentage of nodes for visualization, but does not specify train/validation/test splits for model training or evaluation. |
| Hardware Specification | No | The paper does not provide specific hardware details used for running its experiments. |
| Software Dependencies | No | The paper does not provide specific ancillary software details with version numbers. |
| Experiment Setup | Yes | Figure 3 shows the final results for Convex Combination, Geometric, and Exponential PEFs parametrised by α {0, 0.1, . . . , 0.9, 1.0}. Specifically, we generated 75 random graphs based on 2-dimensional grids [0, 80] [0, 80]. Here, every cell is a node in the graph and only cells in the same column or row are connected by an edge. Each edge is created with the probability 0.25 and its weight is the Cartesian distance between those cells. |