RETRACTED ARTICLE: Spatial-temporal deep learning model based rumor source identification in social networks

Qiufen Ni, Xihao Wu, Hui Chen, Rong Jin and Huan Wang ()
Additional contact information
Qiufen Ni: Guangdong University of Technology
Xihao Wu: Guangdong University of Technology
Hui Chen: Guangdong University of Technology
Rong Jin: California State University
Huan Wang: Huazhong Agricultural University

Journal of Combinatorial Optimization, 2023, vol. 45, issue 3, No 4, 22 pages

Abstract: Abstract Rumor source detection has long been an important but difficult problem. Due to the complexity of the underlying propagation model, most existing methods only rely on the limit observation of a single batch of single snapshot during the propagation process in the spatial graph networks, which neglects temporal dependency and temporal features of the rumor propagation process. Taking multiple batches of multiple snapshots as input can reveal the temporal dependency. Inspired by the traditional spatial-temporal graph convolution network (STGCN), which is a model that can combine spatial and temporal features. In this paper, we propose a STGCN based model called Spatio-Temporal Approximate Personalized Propagation of Neural Predictions (STAPPNP), which firstly learns both the spatial and temporal features automatically from multiple batches of multiple snapshots to locate the rumor source. As there are no input algorithms that are suitable for multiple batches of multiple snapshots to capture the feature of nodes’ connectivity in STAPPNP, we develop an input algorithm to generate a 4-dimensional input matrix from the multiple batches of multiple snapshots to feed the proposed model. Nonetheless, for deep learning models, such input of multiple batches of multiple snapshots requires multiple convolutional layers to extract spatial features. Too many convolutional layers can lead to over-smoothing and long training time. To address these issues, we improve the Spatio-Temporal-Convolutional(ST-Conv) block, in which we adopt the approximate personalized propagation of neural predictions in the spatial convolutional layer of STAPPNP. Our experimental results show that the accuracy of the rumor source detection is improved by using STAPPNP, and the speed of the training process of STAPPNP outperforms state-of-the-art deep learning approaches under the popular epidemic susceptible-infected (SI) and susceptible-infected-recovery (SIR) models in social networks.

Keywords: Rumor source detection; Deep learning; Spatio-temporal graph convolutional networks; Social network (search for similar items in EconPapers)
Date: 2023
References: View complete reference list from CitEc
Citations:

Downloads: (external link)
http://link.springer.com/10.1007/s10878-023-01018-5 Abstract (text/html)
Access to the full text of the articles in this series is restricted.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:spr:jcomop:v:45:y:2023:i:3:d:10.1007_s10878-023-01018-5

Ordering information: This journal article can be ordered from
https://www.springer.com/journal/10878

DOI: 10.1007/s10878-023-01018-5

Access Statistics for this article

Journal of Combinatorial Optimization is currently edited by Thai, My T.

More articles in Journal of Combinatorial Optimization from Springer
Bibliographic data for series maintained by Sonal Shukla () and Springer Nature Abstracting and Indexing ().