Multi-Scale Spectral Decomposition of Massive Graphs
Authors: Si Si, Donghyuk Shin, Inderjit S Dhillon, Beresford N Parlett
NeurIPS 2014 | Venue PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Experimental results are shown in Section 4 followed by conclusions in Section 5. |
| Researcher Affiliation | Academia | Si Si Department of Computer Science University of Texas at Austin EMAIL Donghyuk Shin Department of Computer Science University of Texas at Austin EMAIL Inderjit S. Dhillon Department of Computer Science University of Texas at Austin EMAIL Beresford N. Parlett Department of Mathematics University of California, Berkeley EMAIL |
| Pseudocode | Yes | Algorithm 1: MSEIGS with single level |
| Open Source Code | No | The paper mentions using open-source tools like Metis and Graclus, but it does not explicitly state that the source code for the proposed MSEIGS method itself is openly available or provide a link to a repository. |
| Open Datasets | Yes | Summary of the datasets is given in Table 1, where the largest graph contains more than 3.6 billion edges. We use the average of the cosine of principal angles cos( ( Uk, Uk)) as the evaluation metric... (a) Cond Mat (b) Amazon (c) Road CA (d) Live Journal (e) Friendster Sub (f) SDWeb. In Table 2, we compare MSEIGS and MSEIGS-Early with other methods for label propagation on two public datasets: Aloi and Delicious... The datasets are referenced with citations such as [17, 28] and [16]. |
| Dataset Splits | Yes | We evaluated the recommendation performance on three publicly available datasets shown in Table 6 (see Appendix 6.7 for more details). We report recall-at-N with N = 20 averaged over 5-fold cross-validation, which is a widely used evaluation metric for top-N recommendation tasks [2]. |
| Hardware Specification | No | The paper states, 'on a single-core machine' and 'Using 16 cores' but does not specify any particular hardware components such as CPU or GPU models, processor types, or memory specifications. |
| Software Dependencies | No | The paper mentions software tools like 'Matlab s eigs function (EIGS) [14]', 'PROPACK [12]', 'randomized SVD (RSVD) [7]', 'block Lanczos (Blk Lan) [21]', and 'Intel MKL', as well as clustering software such as 'Graclus [5], Metis [11], Nerstrand [13] and GEM [27]', but it does not provide specific version numbers for these components. |
| Experiment Setup | No | The paper states, 'The experimental settings can be found in Appendix 6.5.' indicating that specific setup details like hyperparameters are not present in the main body of the paper. |