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Meanwhile, Graph Learning refers to the applications of machine learning models on graph data. With the rapid development in recent decades, graph learning has proven to be of great significance because of its wide applications. Graph learning-based fake review detection has thus been proposed and studied extensively in recent years. There are the following benefits of graph learning-based fake review detection. Firstly, most data in fake review detection contain rich relationships with each other; thus, the graphs effectively leverage the inter-connectivity in these real-world data (Yuan et al., 2017; Yu et al., 2017; Ma et al., 2022). Graphs powerfully capture the correlations among inter-dependent data objects. This nature is even more obvious in online review systems where users, items, attributes, and context are tightly associated with and impact each other by relations (Jerripothula et al., 2020; Rossi et al., 2020; Wang et al., 2020; Liu et al., 2021d). A variety of graphs are generated from data in review systems, and they significantly improve the performance of fake review detection. Second, graph learning effectively learns complicated relations and extracts knowledge from different kinds of graphs (Xu et al., 2020; Wang W. et al., 2021; Xia et al., 2021a). The objective of graph learning is to extract required features from graphs; then, the graph representation is applied for specific tasks (Guo et al., 2021; Xia and Ku, 2021; Xia et al., 2021b; Liu J. et al., 2022; Wang, 2022). In detail, many graph learning techniques, such as graph neural networks (GNNs), have been developed to learn the specific type of relations in the graph models and have been proven effective. Therefore, it is sensible to employ graph learning to model various relations in online review systems. 2.2.1. Fake Review Detection🥿
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Sort Hi there, Disclaimer First: I am one of the founders Notion Press Publishing. Absoutely. When you publish through a platform like Wattpad, you still retain the rights to your work. Which means that you can take it and publish it anywhere else without any problem. Only traditional publishers buy over the rights to your book before publishing it.😚
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