Clustering Signed Networks with the Geometric Mean of Laplacians
Authors: Pedro Mercado, Francesco Tudisco, Matthias Hein
NeurIPS 2016 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | In Section 4 we analyze our and existing approaches for the stochastic block model. In Section 5 we introduce our efficient algorithm to compute eigenvectors of the geometric mean of two matrices, and finally in Section 6 we discuss performance of our approach on real world graphs. Experiments on Wikipedia signed network. We consider the Wikipedia adminship election dataset from [17], which describes relationships that are positive, negative or non existent. We use Algs. 1 3 and look for 30 clusters. Experiments on UCI datasets. We evaluate our method LGM (Algs. 1 3) against LSN, LBN, and LAM with datasets from the UCI repository (see Table. 2). |
| Researcher Affiliation | Academia | 1Saarland University, Saarbrücken, Germany 2University of Padua, Padua, Italy |
| Pseudocode | Yes | Algorithm 1: Spectral clustering with LGM on signed networks; Algorithm 2: IPM applied to A#B.1/2; Algorithm 3: EKSM for the computation of (A 1B) 1/2y |
| Open Source Code | No | The paper does not provide concrete access to source code for the methodology described in this paper. It references a third-party toolbox ([2] D. Bini and B. Ianazzo. The Matrix Means Toolbox. http://bezout.dm.unipi.it/ software/mmtoolbox/, May 2015.) but does not state that its own implementation code is released. |
| Open Datasets | Yes | Experiments on Wikipedia signed network. We consider the Wikipedia adminship election dataset from [17], which describes relationships that are positive, negative or non existent. [17] J. Leskovec and A. Krevl. SNAP Datasets: Stanford Large Network Dataset Collection. http://snap.stanford.edu/data, June 2014. Experiments on UCI datasets. We evaluate our method LGM (Algs. 1 3) against LSN, LBN, and LAM with datasets from the UCI repository (see Table. 2). |
| Dataset Splits | No | The paper does not provide specific dataset split information (exact percentages, sample counts, or citations to predefined splits) for training, validation, or testing. It mentions |
| 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. It only mentions that experiments are performed using "one thread" and refers to "Matlab's eigs". |
| Software Dependencies | No | The paper does not provide specific ancillary software details with version numbers. It mentions "Matlab's eigs" and references "The Matrix Means Toolbox" but without specific version numbers for these software components. |
| Experiment Setup | Yes | Experiments are done with all four parameters discretized in [0, 1] with 100 steps. In all cases we consider clusters of size |C| = 100 and present the median of clustering error (i.e., error when clusters are labeled via majority vote) of 50 runs. We use Algs. 1 3 and look for 30 clusters. For each dataset we test all clustering methods over all possible choices of k+, k {3, 5, 7, 10, 15, 20, 40, 60}. In the figure on the right of Table 2 we present the clustering error on MNIST dataset fixing k+ = 10. |