Conference proceeding
DeepDiffuse: Predicting the 'Who' and 'When' in Cascades
2018 IEEE International Conference on Data Mining (ICDM), Vol.2018-, pp.1055-1060
11/2018
DOI: 10.1109/ICDM.2018.00134
Abstract
Cascades are an accepted model to capturing how information diffuses across social network platforms. A large body of research has been focused on dissecting the anatomy of such cascades and forecasting their progression. One recurring theme involves predicting the next stage(s) of cascades utilizing pertinent information such as the underlying social network, structural properties of nodes (e.g., degree) and (partial) histories of cascade propagation. However, such type of granular information is rarely available in practice. We study in this paper the problem of cascade prediction utilizing only two types of (coarse) information, viz. which node is infected and its corresponding infection time. We first construct several simple baselines to solve this cascade prediction problem. Then we describe the shortcomings of these methods and propose a new solution leveraging recent progress in embeddings and attention models from representation learning. We also perform an exhaustive analysis of our methods on several real world datasets. Our proposed model outperforms the baselines and several other state-of-the-art methods.
Details
- Title: Subtitle
- DeepDiffuse: Predicting the 'Who' and 'When' in Cascades
- Creators
- Mohammad Raihanul Islam - Virginia TechSathappan Muthiah - Virginia TechBijaya Adhikari - Virginia TechB. Aditya Prakash - Virginia TechNaren Ramakrishnan - Virginia Tech
- Resource Type
- Conference proceeding
- Publication Details
- 2018 IEEE International Conference on Data Mining (ICDM), Vol.2018-, pp.1055-1060
- Publisher
- IEEE
- DOI
- 10.1109/ICDM.2018.00134
- ISSN
- 1550-4786
- eISSN
- 2374-8486
- Language
- English
- Date published
- 11/2018
- Academic Unit
- Computer Science
- Record Identifier
- 9984259482602771
Metrics
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