From random walks to distances on unweighted graphs
Authors: Tatsunori Hashimoto, Yi Sun, Tommi Jaakkola
NeurIPS 2015 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Tests on simulated and real-world data show that the LTHT matches theoretical predictions and outperforms alternatives. |
| Researcher Affiliation | Academia | Tatsunori B. Hashimoto MIT EECS thashim@mit.edu Yi Sun MIT Mathematics yisun@mit.edu Tommi S. Jaakkola MIT EECS tommi@mit.edu |
| Pseudocode | No | The paper does not contain any structured pseudocode or algorithm blocks. |
| Open Source Code | Yes | Code to generate figures in this paper are contained in the supplement. |
| Open Datasets | Yes | The KDD 2003 challenge dataset [5] includes a directed, unweighted network of e-print ar Xiv citations whose dense connected component has 11,042 vertices and 222,027 edges. ... The Edinburgh associative thesaurus [7] is a network with a dense connected component of 7754 vertices and 246,609 edges in which subjects were shown a set of ten words and for each word was asked to respond with the first word to occur to them. |
| Dataset Splits | No | The paper describes how edges are deleted and compared for link prediction tasks, but does not provide specific percentages or counts for training, validation, or test splits. It implicitly uses a form of cross-validation or hold-out testing by evaluating on deleted edges. |
| Hardware Specification | No | The paper does not provide any specific hardware details (e.g., GPU/CPU models, memory) used for running experiments. |
| Software Dependencies | No | The paper does not provide specific software details with version numbers (e.g., library names with versions) needed to replicate the experiment. |
| Experiment Setup | Yes | We consider two separate link prediction tasks on the largest connected component of vertices of degree at least five, fixing β = 0.2. |