Multiple Source Detection without Knowing the Underlying Propagation Model
Authors: Zheng Wang, Chaokun Wang, Jisheng Pei, Xiaojun Ye
AAAI 2017 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Extensive experiments are conducted on several real-world datasets to demonstrate the effectiveness of the proposed method. |
| Researcher Affiliation | Academia | Zheng Wang, Chaokun Wang, Jisheng Pei, Xiaojun Ye School of Software, Tsinghua University, Beijing 100084, P.R. China Tsinghua National Laboratory for Information Science and Technology (TNList) |
| Pseudocode | Yes | Algorithm 1 Label Propagation based Source Identification (LPSI) |
| Open Source Code | No | The paper thanks Dr. B. Aditya Prakash for providing the source code of Net Sleuth (a comparative method), but does not state that the code for their own method (LPSI) is open-source or publicly available. |
| Open Datasets | Yes | As stated in Table 1, we use the following three real-world datasets: KARATE (Zachary 1977), Jazz (Gleiser and Danon 2003), Ego-Facebook (Leskovec and Mcauley 2012) |
| Dataset Splits | No | The paper describes how infected networks are generated for evaluation and mentions parameters like 'max infect rate to 50%', but it does not specify traditional training/validation/test dataset splits (e.g., percentages, sample counts, or predefined splits) for model development or hyperparameter tuning. |
| Hardware Specification | Yes | The program runs on a server with Intel(R) Core(TM) i7-2600 3.40GHz CPU and 32 GB memory. |
| Software Dependencies | No | The paper states, 'All algorithms are implemented in Matlab.' However, it does not provide a specific version number for Matlab or any other software dependencies. |
| Experiment Setup | Yes | In both LPSI con and LPSI iter, we set the parameter α=0.5. In addition, to show the effectiveness of LPSI iter, its iteration number is set to a small one (5 in this study). |