CoreCluster: A Degeneracy Based Graph Clustering Framework
Authors: Christos Giatsidis, Fragkiskos Malliaros, Dimitrios Thilikos, Michalis Vazirgiannis
AAAI 2014 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Experimental Evaluation Here we present the experimental results of our framework on both the amelioration of the execution time and the quality of the clustering results. |
| Researcher Affiliation | Academia | Christos Giatsidis Ecole Polytechnique giatsidis@lix.polytechnique.fr Fragkiskos D. Malliaros Ecole Polytechnique fmalliaros@lix.polytechnique.fr Dimitrios M. Thilikos CNRS, LIRMM and Uo A sedthilk@thilikos.info Michalis Vazirgiannis Ecole Polytechnique and AUEB mvazirg@lix.polytechnique.fr |
| Pseudocode | Yes | Procedure CORECLUSTER(G). Input: A graph G. Output: A partition of V (G) into clusters. 1. k := δ (G). 2. q := 0. 3. Let Vk, . . . , V0 be the core expansion sequence of G. 4. For i = 0, . . . , k, let Gi be the i-core of G, 5. Let Sk = Vk. 6. Let Ak = {Ck 1 , . . . , Ck k} = Cluster(G[Sk]). 7. for i = k 1 to 0 do 8. Si =Select(Gi, Ak [ . . . [ Ai+1, Vi), 9. let Ai = (Ci 1, . . . , Ci i) = Cluster(G[Si]). 10. Return Ak [ [ A0. |
| Open Source Code | No | The paper refers to 'Supplemental material: Core Cluster: A degeneracy based graph clustering framework.' at http://www.lix.polytechnique.fr/dascim/wp-content/uploads/papers/corecluster14_supplemental.pdf, but does not explicitly state that source code for their methodology is provided there or at another location. |
| Open Datasets | Yes | We exploit the graph generator proposed in (Lancichinetti, Fortunato, and Radicchi 2008) to produce graphs with ground truth clustering structure. ... We also perform evaluations to a subset of the Facebook dataset (Traud, Mucha, and Porter 2011). |
| Dataset Splits | No | The paper describes the datasets used (artificial and Facebook) and evaluation metrics (NMI, Conductance), but does not explicitly detail specific train/validation/test splits used for the experiments. |
| Hardware Specification | No | The paper mentions 'hardware limitation' but does not provide specific details about the CPU, GPU, memory, or other hardware used for the experiments. |
| Software Dependencies | No | The paper mentions using 'Ng-Jordan-Weiss spectral clustering algorithm' and 'k-means++', but does not specify any software dependencies with version numbers (e.g., Python, PyTorch, scikit-learn versions). |
| Experiment Setup | Yes | Based on parameter space exploration, the values for the parameters of the Select procedure are chosen to be a = 0.8 and β = 5 (this choice appears to work optimally in our experiments). |