Notice: The reproducibility variables underlying each score are classified using an automated LLM-based pipeline, validated against a manually labeled dataset. LLM-based classification introduces uncertainty and potential bias; scores should be interpreted as estimates. Full accuracy metrics and methodology are described in Coakley et alK. L. Coakley, T. Snelleman, H. Hoos, and O. E. Gundersen, "The embrace of open science: An analysis of a decade of AI research and 56 800 conference papers," Under Review, 2026..
p-Norm Flow Diffusion for Local Graph Clustering
Authors: Kimon Fountoulakis, Di Wang, Shenghao Yang
ICML 2020 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We show that the proposed problem can be solved in strongly local running time for p 2 and conduct empirical evaluations on both synthetic and real-world graphs to illustrate our approach compares favorably with existing methods. |
| Researcher Affiliation | Collaboration | 1School of Computer Science, University of Waterloo, Canada 2Google Research, USA. Correspondence to: Kimon Fountoulakis <EMAIL>, Di Wang <EMAIL>, Shenghao Yang <shenghao.uwaterloo.ca>. |
| Pseudocode | Yes | Algorithm 1 Coordinate solver for smoothed dual problem |
| Open Source Code | Yes | The code is available at github.com/s-h-yang/ p Norm Flow Diffusion. |
| Open Datasets | Yes | First, we carry out experiments on various LFR synthetic graphs (Lancichinetti et al., 2008)... The other four datasets are real-world graphs: the Facebook college graphs of John Hopkins (FB-Johns55) and Colgate (Colgate88) (Traud et al., 2012)), the social network Orkut (Yang & Leskovec, 2012)) and the biological network SīŦd (Brown et al., 2006). |
| Dataset Splits | No | The paper describes how synthetic graphs are generated and how real-world clusters are filtered and analyzed (e.g., 'For each graph, we start from a random seed node and we repeat the experiment 100 times.'), but it does not provide specific train/validation/test dataset splits or cross-validation details. |
| Hardware Specification | No | The paper does not provide any specific details about the hardware (e.g., GPU/CPU models, memory specifications) used to run the experiments. |
| Software Dependencies | No | The paper states 'We implemented Algorithm 1 in Julia5', but it does not specify any other software dependencies, libraries, or solvers with version numbers. |
| Experiment Setup | Yes | We set the parameter for p-norm diffusion so | | is a constant factor of the volume of some target cluster... We use the same parameter setting of nonlinear power diffusion as what the authors suggested (Ibrahim & Gleich, 2019). For â1-regularized Page Rank, we allow it to cheat in the sense that we use ground truth to choose the parameter giving the best conductance result. |