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A Survey of Information Cascade Analysis: Models, Predictions, and Recent Advances
TLDR
The deluge of digital information in our daily life—from user-generated content, such as microblogs and scientific papers, to online business such as viral marketing and advertising—offers unprecedented opportunities to explore the trajectories and structures of the evolution of information cascades. Expand
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Variational Information Diffusion for Probabilistic Cascades Prediction
TLDR
We propose a novel probabilistic cascade prediction framework: Variational Cascade (VaCas) graph learning networks. Expand
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A Heterogeneous Dynamical Graph Neural Networks Approach to Quantify Scientific Impact
TLDR
We propose an approach based on Heterogeneous Dynamical Graph Neural Network (HDGNN) to explicitly model and predict the cumulative impact of papers and authors. Expand
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Unsupervised User Identity Linkage via Graph Neural Networks
TLDR
We introduce a novel UIL model NWUIL (Network Wasserstein learning for UIL) to identify anchor users across social networks in a fully unsupervised manner. Expand
Meta-Learned User Preference for Topic Participation Prediction
TLDR
We present a novel method MetaTP (Meta learning based Topic Prediction) for exploiting the complex preference of users over the topics and identify the potential topics for cold-start users. Expand
Continual Information Cascade Learning
TLDR
Modeling the information diffusion process is an essential step towards understanding the mechanisms driving the success of information. Expand