Hyperparametric Robust and Dynamic Influence Maximization
Authors: Arkaprava Saha, Bogdan Cautis, Xiaokui Xiao, Laks V. S. Lakshmanan
AAAI 2025 | Venue PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | 5 Experiments We evaluate next RIME s effectiveness and efficiency. It was implemented in C++, run on a Linux server with 2 GHz AMD CPU, 512GB RAM. Other experiments on parameter sensitivity or efficiency / effectiveness on larger or different kinds of networks are provided in the extended version. Networks & Hyperparameter. We generate synthetic random networks with n0 = 10x, x {2, 3, 4, 5}, and m0 = 2n0. We also test our methods on the real-world Weibo social network (Zhang et al. 2013). |
| Researcher Affiliation | Academia | 1Des Cartes Program, CNRS@CREATE, Singapore 2Laboratoire d Informatique de Grenoble, Universit e Grenoble Alpes, Grenoble, Auvergne-Rhˆone-Alpes, France 3Laboratoire Interdisciplinaire des Sciences du Num erique, Universit e Paris-Saclay, Paris, ˆIle-de-France, France 4School of Computing, National University of Singapore, Singapore 5Department of Computer Science, University of British Columbia, Vancouver, British Columbia, Canada |
| Pseudocode | Yes | Algorithm 1: RESTART, Algorithm 2: FIND-SEEDS, Algorithm 3: GREEDY (INSERTION), Algorithm 4: INSERT-NODE, Algorithm 5: INSERT-EDGE |
| Open Source Code | Yes | Code and Datasets https://github.com/Arka Saha/RDIM |
| Open Datasets | Yes | We also test our methods on the real-world Weibo social network (Zhang et al. 2013). |
| Dataset Splits | No | The paper mentions generating synthetic random networks and testing on the Weibo social network. It describes extracting the largest connected component of the subgraph for Weibo, but does not provide specific details on how these datasets were partitioned into training, validation, or test sets in terms of percentages or sample counts for the experiments. |
| Hardware Specification | Yes | It was implemented in C++, run on a Linux server with 2 GHz AMD CPU, 512GB RAM. |
| Software Dependencies | No | The paper states, "It was implemented in C++," but does not specify any libraries, frameworks, or solvers with their respective version numbers that are critical for replicating the experiments. |
| Experiment Setup | No | The paper mentions generating synthetic networks with n0 = 10x, x {2, 3, 4, 5}, and m0 = 2n0, and describes the hyperparameter space for the diffusion model. However, it does not explicitly state concrete hyperparameter values or training configurations for the RIME algorithm itself (e.g., specific values for T, l, epsilon, learning rates, or the budget k for the seed set) that were used in the reported experiments within the main text. |