MSR: A Multifaceted Self-Retrieval Framework for Microscopic Cascade Prediction
Authors: Dongsheng Hong, Chao Chen, Xujia Li, Shuhui Wang, Wen Lin, Xiangwen Liao
AAAI 2025 | Venue PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Empirical evaluations on public datasets demonstrate that this framework significantly outperforms baselines on Hits@κ and MAP@κ, affirming its enhanced ability. ... Empirical Validation: Our method shows significant improvements over baseline models, with an average increase of 6.0% on Hits@κ and 4.4% on MAP@κ across three public datasets. Additionally, the ablation studies, visualizations, and parameter analysis further validate the effectiveness and robustness of each component. |
| Researcher Affiliation | Academia | 1 College of Computer and Data Science, Fuzhou University, China 2 School of Computer Science and Technology, Harbin Institute of Technology (Shenzhen), China 3 Hong Kong University of Science and Technology, Hong Kong SAR, China 4 College of Computer and Control Engineering, Minjiang University, China EMAIL, EMAIL, EMAIL, EMAIL |
| Pseudocode | No | The paper describes its methodology using mathematical formulations and descriptive text, but does not include any clearly labeled pseudocode or algorithm blocks. |
| Open Source Code | No | The paper does not contain any explicit statements about releasing source code, nor does it provide links to a code repository. |
| Open Datasets | Yes | We experimented with the following datasets. Twitter (Hodas and Lerman 2014): Includes retweet records from 2010, with cascades formed by retweets. Memetracker (Leskovec, Backstrom, and Kleinberg 2009): Forms cascades of identical memes from social media, without explicit follower relationships. Douban (Zhong et al. 2012): Comprises cascades based on comments for movie and book reviews. |
| Dataset Splits | Yes | In alignment with previous studies (Sun et al. 2022; Wang et al. 2022; Li et al. 2023), we partitioned datasets into training, validation and test at a ratio of 8:1:1. |
| Hardware Specification | No | The paper does not provide specific details about the hardware used to run the experiments, such as GPU or CPU models. |
| Software Dependencies | No | The paper mentions various components and techniques like GNNs, GRAU, GRU, GCN, etc., but does not specify software names with their version numbers (e.g., Python, PyTorch, TensorFlow versions). |
| Experiment Setup | Yes | Given the importance of the temperature coefficient τ, we also conducted a parameter selection experiment to evaluate its impact on the model s performance. ... The result indicates that the model s performance stabilizes when τ is within the range of 0.05 to 0.1, with the optimal performance observed at τ = 0.02. ... Consequently, 0.05 is identified as yielding the best performance and is thus selected as the default setting for our method. ... The log-cross entropy loss is used for training. |