Abstract
With the ubiquity of electronic communication devices, detecting the information sources is a critical task in reducing the damage caused by malicious sources. However, in the contemporary research of sources identifications and information propagation identifications are calculated through social network topology structure or mathematics inference. In this paper, we borrow the training tool of neural network and propose a deep convolutional neural network to identify the sources in social networks. Initially, we utilize the 20% of data set to play the role of training set and substitute into the proposed model. Subsequently, we employ a bi-graph to classify the trained sources into truth or rumor vertexes. Finally, we utilize our proposed model to test 80% of data set as evaluation results of our identification mechanism. From the experimental results, our developed method can identify more than 85% of information sources and the classification accuracy can reach 80% in both test and train process. The obtained results further indicate that our model can effectively and accurately identify the information sources with reasonable computation costs.
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Wang, J., Ye, J., Mou, W., Li, R., Xu, G. (2022). Information Sources Identification in Social Networks Using Deep Convolutional Neural Network. In: Wang, L., Segal, M., Chen, J., Qiu, T. (eds) Wireless Algorithms, Systems, and Applications. WASA 2022. Lecture Notes in Computer Science, vol 13473. Springer, Cham. https://doi.org/10.1007/978-3-031-19211-1_17
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DOI: https://doi.org/10.1007/978-3-031-19211-1_17
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