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Article summary:
1. This article presents a novel method called minority-weighted graph neural network (mGNN) for imbalanced node classification in social networks of the Internet of People.
2. The mGNN uses a node feature aggregation stage to calculate node membership values among nodes and an oversampling stage to improve edge prediction results of synthetic minority nodes.
3. Experimental results show that the proposed mGNN yields promising results for imbalanced node classification on six social network data sets.
Article analysis:
The article is generally trustworthy and reliable, as it provides detailed information about the proposed method and its evaluation on six social network data sets. The authors have provided evidence for their claims, such as experimental results showing that the proposed mGNN yields promising results for imbalanced node classification on six social network data sets. However, there are some potential biases in the article that should be noted. For example, the authors do not explore any counterarguments or present both sides equally when discussing their proposed method. Additionally, there is no mention of possible risks associated with using this method, which could lead to an incomplete understanding of its implications. Furthermore, there is some promotional content in the article which could be seen as biased towards the authors’ own work. In conclusion, while this article is generally trustworthy and reliable, it should be read with caution due to potential biases and missing points of consideration.