How to Build Your Network? A Structural Analysis
Authors: Anastasia Moskvina, Jiamou Liu
IJCAI 2016 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We experimentally test and compare the performance of all our algorithms. We implemented the algorithms using Sage [Stein, 2012]. Experiment 1: Output sizes. We generate 300 graphs whose numbers of nodes vary between 100 and 1000 using each random graph model. Experiment 3: Real-world datasets. We test the algorithms on several real-world datasets: The Facebook dataset, collected from survey participants of Facebook App, consists of friendship relation on Facebook [Mc Auley and Leskovec, 2012]. |
| Researcher Affiliation | Academia | Anastasia Moskvina Auckland University of Technology Auckland, New Zealand anastasia.moskvina@aut.ac.nz Jiamou Liu University of Auckland Auckland, New Zealand jiamou.liu@auckland.ac.nz |
| Pseudocode | Yes | Procedure 1 Imp-Center: Given G = (V, E) (with radius r) |
| Open Source Code | No | The paper states 'We implemented the algorithms using Sage [Stein, 2012].' but does not provide concrete access or an explicit statement about releasing their own source code for the described methodology. |
| Open Datasets | Yes | The Facebook dataset, collected from survey participants of Facebook App, consists of friendship relation on Facebook [Mc Auley and Leskovec, 2012]. Enron is an email network of the company made public by the FERC [Leskovec et al., 2009]. Col1 and Col2 are collaboration networks that represent scientiļ¬c collaborations between authors papers submitted to General Relativity and Quantum Cosmology category (Col1), and to High Energy Physics Theory category (Col2) [Leskovec et al., 2007]. |
| Dataset Splits | No | The paper does not provide specific details on train/validation/test dataset splits, only mentioning the use of various datasets for experiments. |
| Hardware Specification | No | The paper does not provide specific hardware details (e.g., GPU/CPU models, processor types, memory amounts) used for running its experiments. |
| Software Dependencies | Yes | We implemented the algorithms using Sage [Stein, 2012]. |
| Experiment Setup | No | The paper describes generating graphs with varying node numbers (100 to 1000) and general experiment types but does not provide specific experimental setup details such as hyperparameter values or detailed training configurations. |