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].
C3MM: Clique-Closure based Hyperlink Prediction
Authors: Govind Sharma, Prasanna Patil, M. Narasimha Murty
IJCAI 2020 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We perform experiments (both the hypothesis-test as well as the hyperlink prediction) on multiple real datasets, report results, and provide both quantitative and qualitative arguments favouring better performances w.r.t. the state-of-the-art. |
| Researcher Affiliation | Academia | Govind Sharma , Prasanna Patil and M. Narasimha Murty Indian Institute of Science, Bengaluru, India |
| Pseudocode | Yes | Algorithm 1 An algorithm to test CCH on a temporal hypergraph H = (V, F, T). |
| Open Source Code | Yes | 3Code available at https://github.com/govindjsk/c3mm |
| Open Datasets | Yes | We have performed our experiments on altogether ten datasets, of which four are temporal hypergraphs and we use the six non-temporal metabolite hypergraphs from Zhang et al. [Zhang et al., 2018]. ... For more information, we suggest the reader to refer to Benson et al. [Benson et al., 2018] for an extensive analysis of the four (and more) temporal datasets, and to Zhang et al. [Zhang et al., 2018] for the six metabolites datasets. |
| Dataset Splits | Yes | For our experiments, we ο¬rst partition the set of hyperlinks into two parts, namely observed and unobserved hyperlinks (Fobs and Funobs). For temporal hypergraphs, the set of hyperlinks is partitioned chronologically whereas for non-temporal hypergraphs, it is done randomly. ... We sample 15 times as many non-hyperlinks as there are hyperlinks in the unobserved hypergraph for all of the temporal hypergraphs. |
| Hardware Specification | No | The paper does not provide any specific hardware details such as GPU/CPU models or memory used for the experiments. |
| Software Dependencies | No | The paper mentions software components like 'Symmetric NMF' and 'Factorization Machines' but does not provide specific version numbers for any of the software dependencies used in their experiments. |
| Experiment Setup | Yes | We ο¬x the size of latent dimension for symmetric NMF in (6) to be k = 30 for the all the datasets, just as Zhang et al. [Zhang et al., 2018] do as a default choice for CMM. |