Maximizing the Coverage of Information Propagation in Social Networks
Authors: Zhefeng Wang, Enhong Chen, Qi Liu, Yu Yang, Yong Ge, Biao Chang
IJCAI 2015 | Conference PDF | Archive PDF | Plain Text | 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 {zhefwang, chbiao}@mail.ustc.edu.cn , {cheneh, qiliuql}@ustc.edu.cn Simon Fraser University, yya119@sfu.ca University of North Carolina at Charlotte, yong.ge@uncc.edu |
| 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 first 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. |