Causality Based Propagation History Ranking in Social Networks
Authors: Zheng Wang, Chaokun Wang, Jisheng Pei, Xiaojun Ye, Philip S. Yu
IJCAI 2016 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | 6 Experimental Evaluation In this section, we show the effectiveness of the proposed ranking strategy resp-cap by answering the following two questions. Q1: Can its two indicators (i.e., responsibility and capability) partly capture the intuition of causal effects? Q2: Can this integrated ranking strategy capture two faces of causal effects and thus improve performance? |
| Researcher Affiliation | Academia | 1School of Software, Tsinghua University, Beijing 100084, P.R. China 2Tsinghua National Laboratory for Information Science and Technology (TNList) 3Department of Computer Science, University of Illinois at Chicago, U.S.A 4Institute for Data Science, Tsinghua University, Beijing, China {zheng-wang13, pjs07}@mails.tsinghua.edu.cn; {chaokun, yexj}@tsinghua.edu.cn; psyu@uic.edu |
| Pseudocode | Yes | Algorithm 1 Appresp Input: The SCP form of the propagation history Φ, the involved edge set T, and an edge t ∈ T Output: The approximate responsibility of t |
| Open Source Code | No | The paper does not provide an explicit statement about open-sourcing the code or a link to a code repository. |
| Open Datasets | Yes | Dataset. We use a real-world dataset ego-Facebook [Mc Auley and Leskovec, 2012], which contains 4,039 nodes and 88,234 undirected links. |
| Dataset Splits | No | The paper does not explicitly state training/validation/test dataset splits with percentages or counts, nor does it refer to predefined splits with citations for reproducibility. |
| Hardware Specification | No | To obtain the ranking ground truth, we run randomized experiments to get DCE values on four servers (with 8 cores and 32GB memory) for 100–400 hours for each dataset. This description lacks specific CPU/GPU models or detailed processor information. |
| Software Dependencies | No | The paper does not specify any software dependencies with version numbers (e.g., Python, PyTorch, TensorFlow versions, or specific solver versions). |
| Experiment Setup | Yes | In the integrated resp-cap methods, we all set the parameter λ=0.5. |