Efficient layout of large-scale graphs remains a challenging problem: the force-directed and dimensionality reduction-based methods suffer from high overhead for graph distance and gradient computation. In this paper, we present a new graph layout algorithm, called DRGraph, that enhances the nonlinear dimensionality reduction process with three schemes: approximating graph distances by means of a sparse distance matrix, estimating the gradient by using the negative sampling technique, and accelerating the optimization process through a multi-level layout scheme. DRGraph achieves a linear complexity for the computation and memory consumption, and scales up to large-scale graphs with millions of nodes. Experimental results and comparisons with state-of-the-art graph layout methods demonstrate that DRGraph can generate visually comparable layouts with a faster running time and a lower memory requirement.

Nowadays, the application of a UAV swarm is becoming more and more widespread in the military field, and more and more attention is paid to the security of mission resource allocation. However, the relay node forwarding in the wireless transmission process brings greater risks to data leakage, and the computing power and energy of the UAV consumption is limited, so a lighter solution is required. This paper proposes a mechanism for the safe allocation of UAV swarm mission resources based on random labels. Each task has a random label to solve the problem of database security and wireless transmission security in the process of UAV task assignment. Furthermore, a lightweight stream cipher encryption scheme is illustrated to ensure the security of the UAV database. The irreversible hash function SHA‐256 and the lightweight foam structure hash function SPONGENT‐128 are used to generate random labels and then allocate task resources. In the case of energy consumption, it reduces the possibility of the enemy successfully obtaining private data. The simulation results show that the scheme has good performance in terms of security and has better performance than existing methods in terms of throughput and delay, without increasing too much energy consumption.

Understanding the relationship between urban locations is an essential task in urban planning and transportation management. Although prior works have focused on studying urban locations by aggregating location-based properties, our scheme preserves the mutual influence between urban locations and mobility behavior, and thereby enables situation-aware exploration of urban regions. By leveraging word embedding techniques, we encode urban locations with a vectorized representation while retaining situational awareness. Specifically, we design a spatial embedding algorithm that is precomputed by incorporating the interactions between urban locations and moving objects. To explore our proposed technique, we have designed and implemented a web-based visual exploration system that supports the comprehensive analysis of human mobility, location functionality, and traffic assessment by leveraging the proposed visual representation. The case studies demonstrate the effectiveness of our approach.