Effectively predicting the size of an information cascade
is critical for many applications spanning from identifying
viral marketing and fake news to precise recommendation and
online advertising. Traditional approaches either heavily depend
on underlying diffusion models and are not optimized for popularity
prediction, or use complicated hand-crafted features that
cannot be easily generalized to different types of cascades. Recent
generative approaches allow for understanding the spreading
mechanisms, but with unsatisfactory prediction accuracy.
To capture both the underlying structures governing the spread
of information and inherent dependencies between re-tweeting
behaviors of users, we propose a semi-supervised method, called
Recurrent Cascades Convolutional Networks (CasCN), which
explicitly models and predicts cascades through learning the latent
representation of both structural and temporal information,
without involving any other features. In contrast to the existing
single, undirected and stationary Graph Convolutional Networks
(GCNs), CasCN is a novel multi-directional/dynamic GCN. Our
experiments conducted on real-world datasets show that CasCN
significantly improves the prediction accuracy and reduces the
computational cost compared to state-of-the-art approaches.
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Information Cascades Modeling via Deep Multi-Task Learning
Effectively modeling and predicting the information cascades is
at the core of understanding the information diffusion, which is
essential for many related downstream applications, such as fake
news detection and viral marketing identification. Conventional
methods for cascade prediction heavily depend on the hypothesis
of diffusion models and hand-crafted features. Owing to the
significant recent successes of deep learning in multiple domains,
attempts have been made to predict cascades by developing neural
networks based approaches. However, the existing models are not
capable of capturing both the underlying structure of a cascade
graph and the node sequence in the diffusion process which, in
turn, results in unsatisfactory prediction performance. In this paper,
we propose a deep multi-task learning framework with a novel
design of shared-representation layer to aid in explicitly understanding
and predicting the cascades. As it turns out, the learned
latent representation from the shared-representation layer can encode
the structure and the node sequence of the cascade very well.
Our experiments conducted on real-world datasets demonstrate
that our method can significantly improve the prediction accuracy
and reduce the computational cost compared to state-of-the-art
baselines.
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- NSF-PAR ID:
- 10122597
- Date Published:
- Journal Name:
- Proceedings of the 42nd International {ACM} {SIGIR} Conference on Research and Development in Information Retrieval, {SIGIR} 2019, Paris, France, July 21-25, 2019.
- Page Range / eLocation ID:
- 885 to 888
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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- Free Publicly Accessible Full Text
-
Accepted Manuscript1.0
- Conference Paper:
- https://doi.org/10.1145/3331184.3331288
