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 [1].
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. |