Provable Overlapping Community Detection in Weighted Graphs
Authors: Jimit Majmudar, Stephen Vavasis
NeurIPS 2020 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | In this section, we compare the performance of SP+LP on both synthetic and real-world graphs with other popular algorithms. |
| Researcher Affiliation | Academia | Jimit Majmudar University of Waterloo jmajmuda@uwaterloo.ca Stephen Vavasis University of Waterloo vavasis@uwaterloo.ca |
| Pseudocode | Yes | Algorithm 1 SP+LP Input: Matrix P generated according to MMSB, number of communities k Output: Estimated characteristic vectors ˆθ1, . . . , ˆθk [0, 1]n and Algorithm 2 Successive Projection Input: Matrix P generated according to MMSB, number of communities k Output: Estimated set of almost pure nodes J [n] |
| Open Source Code | No | The paper does not provide an explicit statement about the availability of its own source code, nor does it include a link to a code repository. It only mentions using the implementation of the baseline Geo NMF algorithm. |
| Open Datasets | Yes | We consider PPI datasets provided by Krogan et al. [2006] and Collins et al. [2007], which are very popular among the biological community for the protein complex detection problem. The ground truth validation sets used are two standard repositories of protein complexes, which also appear to be the benchmarks in the biological community. These repositories are Munich Information Centre for Protein Sequence (MIPS) and Saccharomyces Genome Database (SGD). |
| Dataset Splits | No | The paper discusses using "standard repositories of protein complexes" (MIPS and SGD) as "ground truth validation sets" for evaluating the detected communities. However, it does not describe specific train/validation/test splits of the input PPI graph data for the purpose of training or hyperparameter tuning of the SP+LP algorithm itself. |
| Hardware Specification | No | The paper reports "wall-clock running time" for experiments but does not provide any specific details about the hardware (e.g., CPU, GPU models, memory) used to conduct these experiments. |
| Software Dependencies | No | The paper does not list specific software dependencies, libraries, or solver versions (e.g., Python, PyTorch, CPLEX) used for implementing or running the SP+LP algorithm. It only mentions using the provided implementation of the baseline Geo NMF. |
| Experiment Setup | Yes | Unless otherwise stated, the default parameter settings are n = 5000, k = 3, α = 0.5, s = n. Figures 1(d) and 1(f) show the performance of the SP+LP for community interaction matrices B with higher off-diagonal elements. More specifically, for those plots, we set B = (1 δ) I + δ ee T . For Figures 1(a), 1(b), 1(c), 1(e), we set B = 0.5 I + 0.5 R where R is a k k diagonal matrix whose each diagonal entry is generated from a uniform distribution over [0, 1]. |