Scalable Fair Influence Maximization
Authors: Xiaobin Rui, Zhixiao Wang, Jiayu Zhao, Lichao Sun, Wei Chen
NeurIPS 2023 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We carry out detailed experimental analysis on five real social networks to study the trade-off between fairness and total influence spread. We test different fairness parameters, influence probabilities, seed budget, and community structures to confirm the performance of our proposed algorithms. |
| Researcher Affiliation | Collaboration | Xiaobin Rui China University of Mining and Technology Xuzhou, Jiangsu, China ruixiaobin@cumt.edu.cn Zhixiao Wang China University of Mining and Technology Xuzhou, Jiangsu, China zhxwang@cumt.edu.cn Jiayu Zhao China University of Mining and Technology Xuzhou, Jiangsu, China zhaojy@cumt.edu.cn Lichao Sun Lehigh University Bethlehem, PA, USA lis221@lehigh.edu Wei Chen Microsoft Research Asia Beijing, China, weic@microsoft.com |
| Pseudocode | Yes | Algorithm 1: RR-Generate: Generate RR sets Algorithm 2: FIMM: Fair Influence Maximization |
| Open Source Code | No | The paper does not contain any explicit statement or link indicating the availability of open-source code for the described methodology. |
| Open Datasets | Yes | Email The Email dataset [29] is generated using email data from a large European research institution... Flixster The Flixster dataset [30] is a network of American social movie discovery services... Amazon The Amazon dataset [31] is collected based on Customers Who Bought This Item Also Bought feature of the Amazon website... Youtube The Youtube dataset [31] is a network of the video-sharing web site... DBLP The DBLP dataset [31] is the co-authorship network... |
| Dataset Splits | No | The paper refers to using datasets but does not provide specific details on how they were split into training, validation, and test sets, or specify cross-validation settings. |
| Hardware Specification | No | The paper does not provide specific details about the hardware (e.g., CPU, GPU models, memory) used for running the experiments. It only mentions the software used for implementation. |
| Software Dependencies | Yes | Note that our algorithm is currently implemented in Matlab 2022a, thus it costs more time to generate RR sets (generating RR sets in C++ could be at least 100 times faster). |
| Experiment Setup | Yes | In all tests, we run 10,000 Monte-Carlo simulations to evaluate both the influence spread and the fair influence under IC model. We also test influence probability p, inequality aversion parameter α and the seed budget k. ... For Email network, we set α = 0.5. ... We test different probabilities that range from 0.001 to 0.01 with the step of 0.001. ... For the Flixster network, we test the inequality aversion parameter α which ranges from 0.1 to 0.9 with the step of 0.1. We set k = 50 for both networks. ... For Amazon, Youtube, and DBLP networks, we set α = 0.5 and p(vi, vj) = 1/din(vj) ... We test different seed budget k that ranges from 5 to 50 with the step of 5. |