Random Walks on Hypergraphs with Edge-Dependent Vertex Weights
Authors: Uthsav Chitra, Benjamin Raphael
ICML 2019 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Finally, we demonstrate the advantages of hypergraphs with edge-dependent vertex weights on ranking applications using realworld datasets. |
| Researcher Affiliation | Academia | 1Department of Computer Science, Princeton University, Princeton, NJ, USA. |
| 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 | Yes | We construct a citation network of all machine learning papers from NIPS, ICML, KDD, IJCAI, UAI, ICLR, and COLT published on or before 10/27/2017, and extracted from the Arnet Miner database (Tang et al., 2008). |
| Dataset Splits | Yes | This dataset contains two kinds of matches: free-for-all matches with up to 8 players, and 1-v-1 matches. There are 31028 free-for-all matches and 5093 1-v-1 matches among 5507 players. Using the free-for-all matches as partial rankings, we construct rankings of all players in the dataset, and evaluate those rankings on the 1-v-1 matches. |
| Hardware Specification | No | The paper does not provide specific hardware details (e.g., exact GPU/CPU models, memory amounts) used for running its experiments. |
| Software Dependencies | No | The paper does not provide specific ancillary software details with version numbers (e.g., library or solver names with version numbers like Python 3.8, CPLEX 12.4). |
| Experiment Setup | Yes | In our experiments, we use a random walk with restart (β = 0.4) instead of just a random walk, so that the stationary distribution always exists (Tong et al., 2006). ... We set the hyperedge and vertex weights to be ω(e) = (standard deviation of scores in match e) + 1, γe(v) = exp[(score of player v in match e)]. ... Instead, we normalize vertex weights so that δ(e) = 1 for all hyperedges e. |