Minimizing Polarization and Disagreement in Social Networks via Link Recommendation
Authors: Liwang Zhu, Qi Bao, Zhongzhi Zhang
NeurIPS 2021 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Extensive experiments on real datasets demonstrate both the efficiency and effectiveness of our algorithms. |
| Researcher Affiliation | Academia | Liwang Zhu, Qi Bao, and Zhongzhi Zhang Shanghai Key Lab of Intelligent Information Processing, Fudan University, Shanghai, China School of Computer Science, Fudan University, Shanghai 200433, China {19210240147, 20110240002, zhangzz}@fudan.edu.cn |
| Pseudocode | Yes | Algorithm 1: SPGREEDY(G, EC, k, s); Algorithm 2: COMP(G, EC, s, ϵ); Algorithm 3: FASTGREEDY(G, EC, s, k, ϵ) |
| Open Source Code | No | The paper states: 'Omitted proofs and implementation are provided as supplementary material.' but does not provide a direct link or explicit confirmation of code availability for the main algorithms (SPGREEDY, FASTGREEDY). It only provides a link to a third-party solver used: 'the Julia implementation of which is available at https://github. com/danspielman/Laplacians.jl.' |
| Open Datasets | Yes | The studied realistic networks are representatively selected from various domains, which are publicly available in the KONECT [31] and SNAP [33]. |
| Dataset Splits | No | The paper does not provide specific training/validation/test splits, as the problem is formulated as an optimization task on existing networks rather than a traditional machine learning classification or regression task. |
| Hardware Specification | Yes | All experiments were conducted on a machine equipped with 32G RAM and 4.2 GHz Intel i7-7700 CPU. |
| Software Dependencies | No | The paper states, 'All algorithms in our experiments are executed in Julia.' and mentions 'the SDDM solver SOLVE [44], the Julia implementation of which is available at https://github. com/danspielman/Laplacians.jl.' However, specific version numbers for Julia or the Laplacians.jl package are not provided in the text. |
| Experiment Setup | Yes | For the approximation algorithm FASTGREEDY, we set ϵ = 0.3. ... We investigate three different distributions for the initial opinions: uniform distribution, exponential distribution, and power-law distribution. |