Dynamic Metric Embedding into lp Space
Authors: Kiarash Banihashem, Mohammadtaghi Hajiaghayi, Dariusz Rafal Kowalski, Jan Olkowski, Max Springer
ICML 2024 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | A. Empirical validation We tested the theoretical algorithm guarantees on three different graphs. |
| Researcher Affiliation | Academia | 1Department of Computer Science, University of Maryland, College Park, USA 2School of Computer and Cyber Sciences, Augusta University, Georgia, USA 3Department of Mathematics, University of Maryland, College Park, USA. |
| Pseudocode | Yes | Algorithm 1 Low-Diameter Randomized Decomposition (LDRD) (Bartal, 1996) and Algorithm 2 Randomized (β, R, ϵ)-Cut Decomposition |
| Open Source Code | No | The paper does not provide an unambiguous statement of releasing code or a direct link to a source-code repository for the methodology described. |
| Open Datasets | Yes | As the backbone for each graph, we used the social network of Last FM users from Asia available in the Stanford Network Analysis Project dataset (SNAP) (Leskovec & Krevl, 2014). |
| Dataset Splits | No | The paper describes the generation of dynamically changing graphs and their augmentation with edge weight changes but does not specify traditional train/validation/test dataset splits. |
| Hardware Specification | No | The paper does not provide specific hardware details (exact GPU/CPU models, processor types, or memory amounts) used for running its experiments. |
| Software Dependencies | No | The paper states that the algorithm was implemented but does not list specific software dependencies with version numbers (e.g., programming languages, libraries, or frameworks). |
| Experiment Setup | Yes | We randomly chose a subset of 150, 300, and 600 connected nodes to form three different bases of the dynamically changing network. We added random weights from a uniform distribution to these graphs. We augmented each graph by respectively 10000, 5000, and 1000 changes to the topology (queries). Each change increases the weight of a randomly and uniformly chosen edge of the graph by a number chosen from a uniform distribution whose range increases as the process progresses. |