Opinion Optimization in Directed Social Networks
Authors: Haoxin Sun, Zhongzhi Zhang
AAAI 2023 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Finally, we execute extensive experiments on various real directed networks, which show that the effectiveness of our two algorithms is similar to each other, both of which outperform several baseline strategies of node selection. |
| Researcher Affiliation | Academia | Shanghai Key Laboratory of Intelligent Information Processing, Fudan University, Shanghai 200433, China School of Computer Science, Fudan University, Shanghai 200433, China 21210240097@m.fudan.edu.cn, zhangzz@fudan.edu.cn |
| Pseudocode | Yes | Algorithm 1: EXACT(G, s, k); Algorithm 2: RANDOMFOREST(G); Algorithm 3: FAST (G, k, l) |
| Open Source Code | No | The paper does not provide any statement or link indicating that the source code for the methodology is openly available. |
| Open Datasets | Yes | The data sets of selected real networks are publicly available in the KONECT (Kunegis 2013) and SNAP (Leskovec and Sosiˇc 2016), the detailed information of which is presented in the first three columns of Table 1. |
| Dataset Splits | No | The paper does not specify training, validation, and test dataset splits. The experiments are conducted on entire real-world networks to evaluate the effectiveness and efficiency of the proposed algorithms in optimizing opinion dynamics, not in a typical supervised learning setup with data partitioning. |
| Hardware Specification | Yes | All our experiments are programmed in Julia using a single thread, and are run on a machine equipped with 4.2 GHz Intel i7-7700 CPU and 32GB of main memory. |
| Software Dependencies | No | The paper mentions that experiments are "programmed in Julia" but does not specify the version number of Julia or any other software libraries with their versions. |
| Experiment Setup | Yes | In our experiment, the number l of samplings in algorithm FAST is set be 500. For each node i, its internal opinion si is generated uniformly in the interval [0, 1]. For each real network, we first calculate the equilibrium expressed opinions of all nodes and their average opinion for the original internal opinions. Then, using our algorithms EXACT and FAST and the four baseline strategies, we select k = 10, 20, 30, 40, 50 nodes and change their internal opinions to 0, and recompute the average expressed opinion associated with the modified internal opinions. |