Deep Learning for Social Network Information Cascade Analysis: a survey

Gao, Liqun; Zhou, Bin; Jia, Yan; Tu, Hongkui; Wang, Ye; Chen, Chenguang; Wang, Haiyang; Zhuang, Hongwu

doi:10.1109/dsc50466.2020.00022

Cited by 4 publications

(3 citation statements)

References 65 publications

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“…Many methods have exploited cascade graphs to improve forwarding prediction (Gao et al 2020;Gao et al 2019;Zhou et al 2021;Chen et al 2019a;Cao et al 2019). Chen et al proposed CasCN leveraging CasLaplacian for modeling information diffusion, which is an extension of directed sensor graph .…”

Section: Related Workmentioning

confidence: 99%

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Fully Exploiting Cascade Graphs for Real-time Forwarding Prediction

Tang

Liao

Huang

et al. 2021

AAAI

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Real-time forwarding prediction for predicting online contents' popularity is beneficial to various social applications for enhancing interactive social behaviors. Cascade graphs, formed by online contents' propagation, play a vital role in real-time forwarding prediction. Existing cascade graph modeling methods are inadequate to embed cascade graphs that have hub structures and deep cascade paths, or they fail to handle the short-term outbreak of forwarding amount. To this end, we propose a novel real-time forwarding prediction method that includes an effective approach for cascade graph embedding and a short-term variation sensitive method for time-series modeling, making the best of cascade graph features. Using two real world datasets, we demonstrate the significant superiority of the proposed method compared with the state-of-the-art. Our experiments also reveal interesting implications hidden in the performance differences between cascade graph embedding and time-series modeling.

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Section: Related Workmentioning

confidence: 99%

“…for improving forwarding prediction (Gao et al 2019;Zhou et al 2021;Gao et al 2020). However, it is still pending to reach a competent cascade graph embedding methods and challenging to facilitate real-time forwarding prediction by leveraging cascade graphs.…”

Section: Introductionmentioning

confidence: 99%

Fully Exploiting Cascade Graphs for Real-time Forwarding Prediction

Tang

Liao

Huang

et al. 2021

AAAI

View full text Add to dashboard Cite

show abstract

“…They are inherently interpretable, but usually fail to provide accurate predictions. Deep learning based models (Gao et al, 2020) predict information popularity by employing recurrent neural networks (RNNs) on sampled diffusion sequences or graph neural networks (GNNs) on cascade graph episodes. We classify these methods without exploiting real time (or explicit) features as implicit time learning.…”

Section: Introductionmentioning

confidence: 99%