Fast Algorithm for K-Truss Discovery on Public-Private Graphs
Authors: Soroush Ebadian, Xin Huang
IJCAI 2019 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Extensive experiments validate the superiority of our proposed algorithms against state-of-the-art methods on realworld datasets. |
| Researcher Affiliation | Academia | Soroush Ebadian1,2 and Xin Huang2 1Sharif University of Technology 2Hong Kong Baptist University soroushebadian@gmail.com, xinhuang@comp.hkbu.edu.hk |
| Pseudocode | Yes | Algorithm 1 Node-Insertion Updating Algorithm; Algorithm 2 Node-Insertion Bound Computing Algorithm; Algorithm 3 Hybrid-PP Algorithm |
| Open Source Code | No | The paper provides a link: '1https://github.com/samjjx/pp-data' in the 'Datasets' section. This link is for the dataset used and not for the source code of the methodology described in the paper. |
| Open Datasets | Yes | Datasets: We used four public-private graphs of PP-DBLP [Huang et al., 2018] in Table 1.1 Published articles make the public network, and ongoing collaborations form the private networks which are only known by partial authors. We also used eight real-world graphs available from SNAP [Leskovec and Krevl, 2014] shown in Table 2. |
| Dataset Splits | No | The paper describes how nodes were sampled and bins created for training the classifier ('We first divided all nodes into 100 100 bins... and then randomly took four nodes from each bin'), but it does not specify explicit train/validation/test splits for the main k-truss discovery problem. |
| Hardware Specification | No | The paper mentions running experiments on 'SNAP graph datasets' and 'PP-DBLP datasets' but does not provide any specific details about the hardware (e.g., CPU, GPU models, memory) used for these experiments. |
| Software Dependencies | No | The paper states that 'Hybrid-PP adopted a Random Forest' as a classifier but does not provide specific version numbers for any software libraries, programming languages, or other dependencies required to reproduce the experiments. |
| Experiment Setup | Yes | We set the parameter k = 7 by default. We also evaluate the methods by varying parameters k in {5, 7, 9, 11, 13, 15}. Hybrid-PP adopted a Random Forest with 51 estimators and a maximum depth of 11 to construct a classifier. |