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].
StructInf: Mining Structural Influence from Social Streams
Authors: Jing Zhang, Jie Tang, Yuanyi Zhong, Yuchen Mo, Juanzi Li, Guojie Song, Wendy Hall, Jimeng Sun
AAAI 2017 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Experiments on a big microblogging dataset show that the proposed sampling algorithms can achieve a 10 speedup compared to the exact in๏ฌuence pattern mining algorithm, with an average error rate of only 1.0%. |
| Researcher Affiliation | Academia | Information School, Renmin University of China Department of Computer Science and Technology, Tsinghua University Key Laboratory of Machine Perception, Peking University Electronics and Computer Science, University of Southampton Computational Science and Engineering at College of Computing, Georgia Institute of Technology |
| Pseudocode | Yes | Algorithm 1: Struct Inf-Basic" and "Algorithm 2: Enum Inf Pattern |
| Open Source Code | Yes | The dataset and code are available online now.3 3http://aminer.org/structinf |
| Open Datasets | Yes | The dataset and code are available online now.3 3http://aminer.org/structinf |
| Dataset Splits | No | The paper mentions training a classifier and using a balanced dataset but does not provide specific train/validation/test dataset splits (percentages, counts, or references to predefined splits). |
| Hardware Specification | No | The paper does not provide specific hardware details such as CPU/GPU models, memory specifications, or cloud instance types used for running experiments. |
| Software Dependencies | No | The paper does not specify any software dependencies with version numbers (e.g., Python version, library versions like TensorFlow, PyTorch, scikit-learn). |
| Experiment Setup | Yes | The results are obtained by executing Struct Inf-S3 with ฯ = 25 hours, q = 0.9, px = 0.6 and py = 0.1, where px and py are the node sampling probabilities for estimating xk and yk respectively, and q is for sampling edges, and is the same for estimating xk and yk. |