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Community structure enhanced cascade prediction
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DOI：10.1016/j.neucom.2019.05.069.png)
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摘要
En 中文
Predicting cascade is a popular issue which can be applied in viral marketing, trending topic detection, network supervision and so on. Conventional methods heavily depend on the hypothesis of diffusion models or hand-crafted rules, which is difficult to generalize to other domains. Recently, researchers attempt to use deep learning methods to circumvent these problems. However, community structure has an important impact on cascade behavior, and almost no researchers take it into cascade prediction tasks. In this paper, we propose a community structure embedded deep learning framework(named by CS-RNN) to enhance the cascade prediction by containing the communist influence in cascade. Extensive experiments on both synthetic and real-world datasets demonstrate the proposed model outperforms state-of-the-art models in next activated nodes and their community structure labels prediction tasks. And parameter sensitivity analysis shows the robustness of our proposed method. (C) 2019 Elsevier B.V. All rights reserved.
Keyword:
Cascade prediction
Deep learning
Community influences
Recurrent neural network
Cascade behavior
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IF:
6.5
论文数:
2.5W
被引数:
6.5W
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