Scalable and Effective Conductance-Based Graph Clustering
Authors: Longlong Lin, Ronghua Li, Tao Jia
AAAI 2023 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Empirical results on real-life and synthetic datasets show that our algorithms can achieve 5 42 times speedup with a high clustering accuracy, while using 1.4 7.8 times less memory than the baseline algorithms. |
| Researcher Affiliation | Academia | Longlong Lin1, Rong-Hua Li 2, Tao Jia1 1College of Computer and Information Science, Southwest University, Chongqing 400715, China 2Beijing Institute of Technology, China |
| Pseudocode | Yes | Algorithm 1: PCon core; Algorithm 2: PCon de |
| Open Source Code | No | The paper does not provide any links to the source code for the described methodology or state that the code is being released. |
| Open Datasets | Yes | We evaluate our proposed solutions on six real-life publiclyavailable datasets2 (Table 2), which are widely used benchmarks for conductance-based graph clustering (Shun et al. 2016; Yang et al. 2019). The maximum connected components of these datasets are used in the experiments. We also use five synthetic graphs LFR (Lancichinetti, Fortunato, and Kert esz 2009), WS (Watts and Strogatz 1998), PLC (Holme and Kim 2002), ER (Erdos, R enyi et al. 1960), and BA (Barab asi and Albert 1999). All datasets can be downloaded from http://snap.stanford.edu/ |
| Dataset Splits | No | The paper uses various datasets for experiments but does not explicitly describe any train/validation/test splits, nor does it refer to standard predefined splits for the specific datasets used. |
| Hardware Specification | No | The paper does not provide any specific hardware details such as CPU/GPU models, memory, or cloud instance types used for running the experiments. |
| Software Dependencies | No | The paper does not list specific software dependencies with version numbers for its implementation. It mentions parameters for baseline methods, but not for the proposed algorithms' software environment. |
| Experiment Setup | No | The paper provides specific parameter settings (e.g., α = 0.01 and ϵ = 1 m for NIBBLE PPR; t = 10 and ϵ = 1 m for HK Relax) for baseline algorithms, but does not specify experimental setup details such as hyperparameters or training configurations for the proposed PCon core and PCon de algorithms. |