Chromatic Correlation Clustering, Revisited
Authors: Qing Xiu, Kai Han, Jing Tang, Shuang Cui, He Huang
NeurIPS 2022 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We conduct extensive experiments using real-world datasets and the results strongly demonstrate the superiorities of Greedy Vote in terms of both clustering quality and efficiency, compared to the state-of-the-art algorithms with or without performance guarantees.In this section, we compare our Greedy Vote (GV) algorithm (i.e., Algorithm 2) with six state-of-the-art algorithms for the CCC problem, including: (1) The Pivot Algorithm [2]; (2) The Reduce and Cluster (RC) Algorithm [3]; (3) The Deep Cluster (DC) algorithm [3]; (4) The Chromatic Ball (CB) algorithm [6]; (5) The Greedy Expansion (GE) Algorithm [25]; and (6) The modified greedy expansion (GER) Algorithm [25]. |
| Researcher Affiliation | Academia | Qing Xiu School of Computer Science and Technology Suzhou Institute for Advanced Research University of Science and Technology of China xiuq@mail.ustc.edu.cnKai Han School of Computer Science and Technology Soochow University hankai@suda.edu.cnJing Tang The Hong Kong University of Science and Technology (Guangzhou) The Hong Kong University of Science and Technology jingtang@ust.hkShuang Cui School of Computer Science and Technology Suzhou Institute for Advanced Research University of Science and Technology of China lakers@mail.ustc.edu.cnHe Huang School of Computer Science and Technology Soochow University huangh@suda.edu.cn |
| Pseudocode | Yes | Algorithm 1: LP-Clustering and Algorithm 2: Greedy Vote |
| Open Source Code | Yes | Did you include the code, data, and instructions needed to reproduce the main experimental results (either in the supplemental material or as a URL)? [Yes] See the supplemental material |
| Open Datasets | Yes | We use ten real-world datasets from different domains in our experiments, as listed in Table 1. Among these datasets, Facebook1, Facebook2, Lastfm and Twitter are social network graphs downloaded from [28], where vertices represent users and edges represent friendships. Following the setting in Anava et al. [3], we assign each social circle a color and set the color of each edge uv as the color of u and v s common social circle (if there are multiple common circles then choose an arbitrary one). The DAWN dataset [1][25] models a drug abuse warning network... The Cooking dataset [23][25] models an ingredient co-occur network... String1 and String2 [12] are networks containing protein-protein interactions... Finally, DBLP and MAG [25] are co-authorship networks... |
| Dataset Splits | No | Section 6 describes the datasets used and some parameter settings for the algorithms, but it does not specify how the datasets were split into training, validation, or test sets. |
| Hardware Specification | Yes | All our experiments are conducted on an Intel(R) Xeon(R) Gold 6126 CPU @ 2.60GHz with 128GB of RAM. |
| Software Dependencies | No | We implement the evaluated algorithms using Python and also use the public code of Klodt et al. [25] for implementation. No specific version numbers for Python or other software libraries are provided. |
| Experiment Setup | Yes | In our experiments, we set ϵ = 0.55 and m = 10 for the GV algorithm, and follow all the parameter settings (if any) of other algorithms according to their proposals. |