A Hybrid Deep Network Representation Model for Detecting Researchers’ Communities | |
| Journal of AI and Data Mining | |
| دوره 10، شماره 2، تیر 2022، صفحه 233-243 اصل مقاله (1.75 M) | |
| نوع مقاله: Original/Review Paper | |
| شناسه دیجیتال (DOI): 10.22044/jadm.2022.11243.2277 | |
| نویسندگان | |
| A. Torkaman1؛ K. Badie2؛ A. Salajegheh* 1؛ M. H. Bokaei3؛ Seyed F. Fatemi4 | |
| 1Department of Computer, South Tehran Branch, Islamic Azad University, Tehran, Iran | |
| 2E-Services and E-Content Research Group, IT Research Faculty, ICT Research Institute, Tehran, Iran | |
| 3Department of Information Technology, ICT Research Institute, Tehran, Iran | |
| 4Sharif University of Technology, Tehran, Iran | |
| چکیده | |
| Recently, network representation has attracted many research works mostly concentrating on representing of nodes in a dense low-dimensional vector. There exist some network embedding methods focusing only on the node structure and some others considering the content information within the nodes. In this paper, we propose HDNR; a hybrid deep network representation model, which uses a triplet deep neural network architecture that considers both the node structure and content information for network representation. In addition, the author's writing style is also considered as a significant feature in the node content information. Inspired by the application of deep learning in natural language processing, our model utilizes a deep random walk method to exploit inter-node structures and two deep sequence prediction methods to extract nodes' content information. The embedding vectors generated in this manner were shown to have the ability of boosting each other for learning optimal node representation, detecting more informative features and ultimately a better community detection. The experimental results confirm the effectiveness of this model for network representation compared to other baseline methods. | |
| کلیدواژهها | |
| community detection؛ deep learning؛ citation network | |
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