Journal
Scientific and Technical Journal of Information Technologies, Mechanics and Optics
UDK004.896
Issue:2 (166)
Ego-net link prediction with GNN (in English)
Annotation
The task of link prediction is one of the key challenges in the field of social network analysis. The common way to build such systems is based on the idea of decomposing a task into two levels. At the first level, links within ego-nets are predicted; at the second, the results are aggregated to form the final predictions. The accuracy of such systems depends on the first-level model. Heuristic methods are usually used here. The focus of this work is on developing a new supervised model to improve the quality of link prediction within ego-nets. The heterogeneity of the edge attributes, the absence of node features, and the dynamic nature of ego-nets distinguish this task from others. The proposed method belongs to the class of graph neural networks. Its key feature is the ability to effectively consider the topology of the graph along with the attributes of the edges, without relying on the properties of the nodes. This effect is achieved by modeling the hidden state of node pairs, rather than the state of each node individually. The iterative nature of the model makes it possible to propagate knowledge about the relationships between nodes, increasing the complexity of the structures considered with each step. To measure the accuracy of the model, the Ego-VK dataset was used. This dataset consists of a set of ego-nets from a subsample of users of the VKontakte social network. The model is compared with the classical Adamic-Adar method as well as modern approaches based on graph neural networks. Experiments show that the proposed model is significantly superior to the baselines with respect to NDCG@5 ranking quality metric. The results demonstrate the high effectiveness of the proposed model, and the possibility of integration into distributed systems makes it widely applicable in the industry.
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