We introduce a new graph sparsification method that targets the neighborhood information available for each node. Our approach is motivated by the fact that neighborhood information is used by several mining and learning tasks on graphs as well as reachability queries. The result of our sparsification technique is a sparsified graph that can be used instead of the original graph in the above tasks while still ensuring fairly good approximations for the results. Moreover, our sparsification method allows users to control the size of the resulting sparsified graph by adjusting the amount of information loss tolerated by the targeted applications. Our extensive experiments conducted on various real and synthetic graphs show that our sparsification considerably reduces the size of the graphs by achieving 40% sparsification rate on average on several input graphs. Furthermore, in the experimental study we show the utility and efficiency of our sparsification algorithm for notable data-driven tasks, such as node classification, graph classification and shortest path approximations.

This paper presents a multi-objective immune memetic algorithm to the challenge of solving the Frequency Assignment Problem (FAP) in cellular networks seeking the minimization of the network’s total interference, the maximum interference and the number of used frequencies. The originality of the proposed approach lies in integrating a FAP-specific local search into its evolutionary process instead of crossover and mutation, as well as a guided diversification strategy for better performances. Moreover, the algorithm is supplemented with a clonal selection, inherited from Artificial Immune Systems (AIS), which aims to improve the algorithm exploration and exploitation abilities. Computational experiments performed over COST259 instances show the efficiency of the newly proposed evolutionary multi-objective algorithm and corroborated by the comparisons we did with the most frequently referred algorithm in the related literature. Furthermore, the effect of the main parameters and the interaction between them is analyzed using statistical tools.