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..
Gradient Method for Continuous Influence Maximization with Budget-Saving Considerations
Authors: Wei Chen, Weizhong Zhang, Haoyu Zhao43-50
AAAI 2020 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We test our algorithms on a real-world dataset and validate its effectiveness comparing with other algorithms. |
| Researcher Affiliation | Collaboration | 1Microsoft Research, Beijing, China 2,3IIIS, Tsinghua University, Beijing, China EMAIL, EMAIL |
| Pseudocode | Yes | Algorithm 1 Grad-RIS: Gradient-RIS Meta-Algorithm for CIM-BS. and Algorithm 2 Sampling Procedure. |
| Open Source Code | No | The paper mentions a full technical report for proofs and experiment results, but does not state that the source code for the described methodology is publicly available or provide a link to it. |
| Open Datasets | Yes | We test on the DM network, which is a network of data mining researchers extracted from the Arnet Miner archive (arnetminer.org), with 679 nodes and 3,374 edges, and edge weights are learned from a topic af๏ฌnity model and obtained from the authors (Tang et al. 2009). |
| Dataset Splits | No | The paper uses the DM network for testing but does not specify any explicit training, validation, or test dataset splits (e.g., percentages or sample counts). |
| Hardware Specification | No | The paper does not provide specific details about the hardware used, such as GPU or CPU models, memory, or cloud instance specifications. |
| Software Dependencies | No | The paper does not provide specific version numbers for any software dependencies used in the experiments. |
| Experiment Setup | Yes | For parameter settings, we set ฮต = 0.1 and โ= 1 for all algorithms. For Greedy-RIS, we set the greedy step size to be 0.1 on each dimension. |