Multi-Scale Spectral Decomposition of Massive Graphs
Authors: Si Si, Donghyuk Shin, Inderjit S Dhillon, Beresford N Parlett
NeurIPS 2014 | Conference 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 ssi@cs.utexas.edu Donghyuk Shin Department of Computer Science University of Texas at Austin dshin@cs.utexas.edu Inderjit S. Dhillon Department of Computer Science University of Texas at Austin inderjit@cs.utexas.edu Beresford N. Parlett Department of Mathematics University of California, Berkeley parlett@math.berkeley.edu |
| 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. |