Abstract
Information diffusion prediction is a fundamental task for understanding information spreading phenomenon. Many of the previous works use static social graph or cascade data for prediction. In contrast, a recently proposed deep leaning model DyHGCN [20] newly considers users’ dynamic preference by using dynamic graphs and achieve better performance. However, training phase of DyHGCN is computationally expensive due to the multiple graph convolution computations. Faster training is also important to reflect users’ dynamic preferences quickly. Therefore, we propose a novel graph convolutional network model with time-based mini-batch (GCNTM) to improve training speed while modeling users’ dynamic preference. Time-based mini-batch is a novel input form to handle dynamic graphs efficiently. Using this input, we reduce the graph convolution computation only once per mini-batch. The experimental results on three real-world datasets show that our model performs comparable results against baseline models. Moreover, our model learns about 5.97 times faster than DyHGCN.
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Notes
- 1.
Although the original paper defined static graph and diffusion graph separately and treat the latter as weighted graph, both graphs are unified and treated as undirected in the authors’ implementation.
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- 3.
Although results of these baselines are reported in [19, 20], we conducted experiment again. These models used an additional user token that denotes the end of cascade sequence and include this token as one of target values. We found that this inclusion improved the results, but this settings were unfair to other baselines. Therefore, we conducted experiments without this token.
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Acknowledgement
This work was supported by JSPS Grant-in-Aid for Scientific Research (B)(Grant Number 17H01785) and JST CREST (Grant Number JPMJCR1687).
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Miyazawa, H., Murata, T. (2021). Graph Convolutional Network with Time-Based Mini-Batch for Information Diffusion Prediction. In: Benito, R.M., Cherifi, C., Cherifi, H., Moro, E., Rocha, L.M., Sales-Pardo, M. (eds) Complex Networks & Their Applications IX. COMPLEX NETWORKS 2020 2020. Studies in Computational Intelligence, vol 944. Springer, Cham. https://doi.org/10.1007/978-3-030-65351-4_5
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