Empowering Random Walk Link Prediction Algorithms in Complex Networks by Adapted Structural Information

In the link prediction problem a relevant algorithm running over a network attempts to determine whether a link between two nodes will exist in the future, given that it is not present at the moment. Most link prediction algorithms take into account the structure of the network on which they are applied and based on this, they attempt to predict the existence or not of future new edges in the network. However, many of them are quite standardized, applying the same concept and parametrization to all networks, thus not always achieving good results in every different network structure. Algorithms based on Graph Neural Networks (GNNs) are more adaptive to any network structure but they do not give appreciable results when the only information available is the network structure. In this paper, we propose a new approach to this problem that approximates the structure of a complex network by allowing adjusted weight to this network structure to create additional information, which we can embed into effective algorithms such as local and superposed random walk link prediction. To achieve this goal, we use well-known kernel functions such as Sigmoids, in which we fit their parameters appropriately by a genetic algorithm to achieve the best possible approximation. To demonstrate the effectiveness of our proposed method we have compared our prediction method results based on precision, AUC and AUPR on eleven selected networks of different structures and properties with seven well-known link prediction algorithms and one more utilizing GNNs. In every case, we have improved the results of random walk algorithms and in most cases we achieved better results from all employed benchmark algorithms.