Profit-Driven Team Grouping in Social Networks
Authors: Shaojie Tang
AAAI 2017 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | In this section, we conduct an empirical evaluation of the proposed algorithms. All experiments were run on a machine with Intel Xeon 2.40GHz CPU and 64GB memory, running 64-bit Red Hat Linux server. |
| Researcher Affiliation | Academia | Shaojie Tang Naveen Jindal School of Management University of Texas at Dallas |
| Pseudocode | Yes | Algorithm 1 Candidate Grouping I |
| Open Source Code | No | The paper does not contain any statement indicating that its source code is publicly available or provide any links to a code repository for the methodology described. |
| Open Datasets | No | The paper states 'We evaluate the proposed algorithms on the real world benchmark dataset collected from Upwork.' but does not provide a direct link, DOI, repository, or a formal citation with author names and year for public access to this specific dataset. |
| Dataset Splits | No | The paper does not provide specific details on dataset splits (e.g., percentages, sample counts, or explicit mentions of training/validation/test sets). |
| Hardware Specification | Yes | All experiments were run on a machine with Intel Xeon 2.40GHz CPU and 64GB memory, running 64-bit Red Hat Linux server. |
| Software Dependencies | No | The paper only mentions 'Red Hat Linux server' for the operating system, but does not provide specific version numbers for any key software components, libraries, or solvers used in the experiments. |
| Experiment Setup | Yes | Recall that in the basic version of the problem, we have the individual load limit fu set to 1, and the project capacity constraint gt set to infinity (i.e., ). We report the total profit achieved by each algorithm, for increasing values of the social connectivity threshold, i.e., ρ ranging from 0 to 1. We also report the extent to which the required number of skills and the social connectivity threshold affect the average size of the teams produced by each algorithm. |