A link prediction method for MANETs based on fast spatio-temporal feature extraction and LSGANs

Abstract

Link prediction aims to learn meaningful features from networks to predict the possibility of topology. Most of the existing research on temporal link prediction is mainly aimed at networks with slow topology changes. They ignore the information of topology interval and link duration. This paper proposes a link prediction model named FastSTLSG. It can automatically analyze the features of the topology in a unified framework to effectively capture the spatio-temporal correlation of Mobile Ad Hoc Networks. First, we regard the changing topology as a chaotic system, transform it into a series of static snapshots based on the autocorrelation function; Next, the fast graph convolutional network efficiently analyses the topological relationships between nodes and reduces the computational complexity by importance sampling. Then, the gate recurrent unit captures the temporal correlation between snapshots. Finally, the fully connected layer reconstructs the topological structure. In addition, we take full advantage of least squares generative adversarial networks to further improve the performance of generator to obtain high-quality link prediction results. Extensive experiments on different datasets show that our FastSTLSG model obtains higher prediction accuracy compared with existing baseline models.

Algorithm for link prediction in self-regulating network with adaptive topology based on graph theory and machine learning

“…Исследование авторов работы [25] посвящено прогнозированию связей в мобильных ad hoc сетях (MANET), решение этой задачи существенно осложняется подвижностью узлов в пространстве. Для предсказания связей авторы предлагают алгоритм, учитывающий длительность связей между узлами, топологию сети и корреляции между узлами.…”