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..
Maximizing the Coverage of Information Propagation in Social Networks
Authors: Zhefeng Wang, Enhong Chen, Qi Liu, Yu Yang, Yong Ge, Biao Chang
IJCAI 2015 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Extensive experiments on three real-world data sets demonstrate the performance of the proposed algorithms. |
| Researcher Affiliation | Academia | School of Computer Science and Technology, University of Science and Technology of China EMAIL , EMAIL Simon Fraser University, EMAIL University of North Carolina at Charlotte, EMAIL |
| Pseudocode | Yes | Algorithm 1: The Lazy-Forward Greedy Algorithm and Algorithm 2: The Effective Degree Rank Algorithm |
| Open Source Code | No | The paper does not provide any specific link or explicit statement about the availability of its source code. |
| Open Datasets | Yes | The three real-world data sets we used are: wiki Vote which is the Wikipedia who-votes-on-whom network, soc-Epinions1 which is the who-trusts-whom network of Epinions.com 1, and weibo which is the who-follows-whom network of Weibo.com 2. The ο¬rst two are downloaded from SNAP3, and the last one is crawled from Weibo.com... |
| Dataset Splits | No | The paper does not provide specific dataset split information (exact percentages, sample counts, citations to predefined splits, or detailed splitting methodology) needed to reproduce the data partitioning into train/validation/test sets. |
| Hardware Specification | Yes | We implemented the algorithms in Java and conducted the following experiments on a Linux server with two 2.0GHz Six-Core Intel Xeon E5-2620 and 96G memory. |
| Software Dependencies | No | The paper mentions implementing algorithms in Java but does not provide specific version numbers for Java or any other ancillary software components or libraries. |
| Experiment Setup | Yes | The propagation probability of an edge (i, j) is set to be weight(i,j) / indegree(j), as widely used in literatures ( [Chen et al., 2009; Goyal et al., 2011b] ). and In the computation process, we run Monte Carlo simulation 10, 000 times to obtain an estimation of the information coverage. |