Notice: The reproducibility variables underlying each score are classified using an automated LLM-based pipeline, validated against a manually labeled dataset. LLM-based classification introduces uncertainty and potential bias; scores should be interpreted as estimates. Full accuracy metrics and methodology are described in Coakley et alK. L. Coakley, T. Snelleman, H. Hoos, and O. E. Gundersen, "The embrace of open science: An analysis of a decade of AI research and 56 800 conference papers," Under Review, 2026..
Fair Influence Maximization: a Welfare Optimization Approach
Authors: Aida Rahmattalabi, Shahin Jabbari, Himabindu Lakkaraju, Phebe Vayanos, Max Izenberg, Ryan Brown, Eric Rice, Milind Tambe11630-11638
AAAI 2021 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Extensive experiments on synthetic and real world datasets including a case study on landslide risk management demonstrate the efficacy of the proposed framework |
| Researcher Affiliation | Academia | 1University of Southern California 2Harvard University 3 RAND Corporation |
| Pseudocode | No | The paper does not contain structured pseudocode or algorithm blocks. |
| Open Source Code | No | The paper mentions a full version at 'https://arxiv.org/abs/2006.07906' which is a link to the paper itself, not to source code. There is no explicit statement about releasing code or a link to a code repository for the methodology described. |
| Open Datasets | No | The paper mentions using 'synthetic and real social networks' and a 'stochastic block model (SBM) networks' and 'in-person semi-structured interview data' for a case study on Sitka, Alaska, which was used to estimate SBM parameters. However, it does not provide concrete access information (e.g., links, DOIs, specific citations with author/year for public datasets) for any of these datasets to be publicly accessed. |
| Dataset Splits | No | The paper does not provide specific dataset split information (e.g., percentages, sample counts, or citations to predefined splits) needed to reproduce data partitioning into training, validation, or test sets. |
| Hardware Specification | No | The paper does not provide specific hardware details (e.g., CPU/GPU models, memory) used for running its experiments. |
| Software Dependencies | No | The paper mentions the Independent Cascade Model but does not specify any software names with version numbers for libraries, frameworks, or solvers used in implementation. |
| Experiment Setup | Yes | We report the average results over 20 random instances and set p = 0.25 in all experiments. ... Figure 2 summarizes results across different budget values K ranging from 2% to 10% of the network size N for our framework (different α values) as well as the baselines. |