Training set of NE-GraphSAGE model

In order to thoroughly assess the generalization and transferability of the NE-GraphSAGE model across different domain datasets, this paper adopts a cross-domain validation strategy. Specifically, two domains with significant inherent differences are selected to construct the training and testing sets, respectively. After training the NE-GraphSAGE model on the non-review paper citation relationship network in Domain A to capture the unique patterns and associations of that domain, the model is then applied to the non-review paper citation relationship network in Domain B. Domain B exhibits evident differences from the training set in terms of data characteristics and research questions, thus creating a challenging testing environment. This strategy of separating the training and testing sets by domain can examine the adaptability and flexibility of the NE-GraphSAGE model when confronted with data from different domains.For this study, the training set is chosen from the intelligent transportation systems domain, while the testing set is selected from the 3D vision domain. Literature, including both review and non-review papers, from 2022 to 2024 in these two research domains is retrieved on the Web of Science platform. The search results yield a review literature collection comprising 473 papers, with 218 in the intelligent transportation systems domain and 255 in the 3D vision domain. The non-review literature collection contains 8311 papers, of which 3276 are in the intelligent transportation systems domain and 5035 are in the 3D vision domain. Based on the citation relationships among the non-review literature, a citation network is constructed and nodes are labeled. Additionally, indicators such as the number of citations, usage frequency, publication year, and the number of research fields covered are embedded as node attribute features. After processing, the training set consists of 1595 nodes and 1784 edges, while the testing set includes 1179 nodes and 908 edges.