Abstract
Predicting the growth size of an information cascade is one of the primary challenges in understanding the diffusion of information. Recent efforts focus on utilizing graph neural networks to capture graph structure. However, there is considerable variance in the information cascade size (from few to million). From the perspective of efficiency and performance, the method of modeling each node is inappropriate for graph neural networks. In this paper, we propose a novel deep learning framework for popularity prediction called CasSampling. Firstly, we exploit a heuristic algorithm to sample the critical part of cascade graph. For the loss of structure information due to sampling, we keep outdegree of sampled node in the global graph as part of the node feature into the graph attention networks. For the loss of temporal information due to sampling, we utilize the time series to learn the global propagation time flow. Then, we design an attention aggregator for node-level representation to better integrate local-level propagation into the global-level time flow. Experiments conducted on two benchmark datasets demonstrate that our method significantly outperforms the state-of-the-art methods for popularity prediction. Additionally, the computation cost is much less than the baselines. Code and (public) datasets are available at https://github.com/Gration-Cheng/CasSampling.
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Acknowledgments
The work was supported by National Natural Science Foundation of China (Grant Nos. 61966020, 61972186, U21B2027), Yunnan high-tech industry development project (Grant No. 201606), Yunnan provincial major science and technology special plan projects (Grant No. 202103AA080015, 202002AD080001-5), Yunnan Basic Research Project (Grant No. 202001AS070014), and Talents and Platform Program of Science and Technology of Yunnan (Grant No. 202105AC160018).
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Ethical Statement
The purpose of this paper is to explore efficient and effective methods for learning cascade graphs for popularity prediction while adhering to academic integrity and research ethics requirements. We used publicly available data from social media datasets that have been authorized by Twitter and Weibo officials. To ensure the confidentiality of personal information, all data is anonymized and stored securely. We obtained approval and permission from the ethics committee of our institution to conduct this research.
The models and algorithms used in this study are based on publicly available data and previous research results, and we have thoroughly tested and verified them. We commit to conducting a transparent and fair evaluation of the algorithms and models used in this research, and we will present them fully in the paper.
Throughout this study, we will adhere to academic standards and ethical requirements, striving to avoid any behavior that may violate these requirements. We hope that this research will contribute to the development of cascade graph learning and popularity prediction, promoting further research in this area.
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Cheng, G., Yan, X., Gao, S., Xu, G., Miao, X. (2023). CasSampling: Exploring Efficient Cascade Graph Learning for Popularity Prediction. In: Koutra, D., Plant, C., Gomez Rodriguez, M., Baralis, E., Bonchi, F. (eds) Machine Learning and Knowledge Discovery in Databases: Research Track. ECML PKDD 2023. Lecture Notes in Computer Science(), vol 14171. Springer, Cham. https://doi.org/10.1007/978-3-031-43418-1_5
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