How to ensure the safe flight of unmanned aerial vehicles (UAVs) in complex environments with static and dynamic obstacles can be a big challenge. Therefore, a new joint path planning method with integrated of an improved non-dominated sorting genetic algorithm (NSGA-III) and an improved artificial potential field (APF) is proposed, which exhibits high computation efficiency and excellent global optimum searching capability. Three objectives including flying efficiency, stability and obstacles avoidance are constructed to meet the strict requirements of the complex environment. Greatest novel features of this new method include three aspects, and they are:1) Hopfield neural network is introduced to replace the random strategy to generate the initial population of NSGA-III, which can enhance the iteration efficiency significantly, 2) Gaussian sampling is designed to create a new potential solution space that is adjacent to the local optimum, which can guide the further searching for a better result and 3) virtual path points as well as tracked distances are both developed in APF to help escape from the stuck area during the dynamic obstacle avoidance. A comprehensive compared study is also carried out by use of popular traditional NSGA-III, improved A∗ and RRT∗ algorithms. The joint planning algorithm can ameliorate path length and flying efficiency by an average of 12.6% and 67.5%, respectively. Simulations and experiments approve the validation.

Nowadays, Multi-purpose Messaging Mobile App (MMMA) has become increasingly prevalent. MMMAs attract fraudsters and some cybercriminals provide support for frauds via black market accounts (BMAs). Compared to fraudsters, BMAs are not directly involved in frauds and are more difficult to detect. This paper illustrates our BMA detection system SGRL (Self-supervised Graph Representation Learning) used in WeChat, a representative MMMA with over a billion users. We tailor Graph Neural Network and Graph Self-supervised Learning in SGRL for BMA detection. The workflow of SGRL contains a pretraining phase that utilizes structural information, node attribute information and available human knowledge, and a lightweight detection phase. In offline experiments, SGRL outperforms state-of-the-art methods by 16.06%-58.17% on offline evaluation measures. We deploy SGRL in the online environment to detect BMAs on the billion-scale WeChat graph, and it exceeds the alternative by 7.27% on the online evaluation measure. In conclusion, SGRL can alleviate label reliance, generalize well to unseen data, and effectively detect BMAs in WeChat.

Instant messaging client (IMC) is now an essential tool for mobile users. In the representative IMC We Chat, cybercriminals deceive frauds, causing financial loss to normal users. Through statistical analysis, we find that certain fraud interactions commonly occur among WeChat users who are not k-hop neighbors. Therefore, efficiently answering whether the distance between two vertices is not longer than k at a certain time point (i.e., k-hop reachability queries) over the dynamic social graph of WeChat becomes a crucial task for fraud feature extraction in the detection system: it can help human experts quickly identify suspicious user interactions and the query results can be further used as the input feature to the downstream machine learning based detection methods. In this paper, we illustrate Bidirectional k-hop Reachability Query Processing over a Dynamic Graph (BREAD) that is used in WeChat for extracting the k-hop reachability feature for fraud detection. BREAD adopts the idea of estimating Personalized PageRank value. It first conducts the backward search from the destination vertex to construct an intermediate vertex set. Then, it performs a certain amount of random walks from the start vertex to see whether they can hit the intermediate vertex set, and the results are returned to answer k-hop reachability queries. We further propose$\text{BREAD}++$that leverages the massive parallel processing power of GPU to achieve a considerable performance gain. Experiments on several large-scale dynamic graph benchmarks and the social graph of WeChat have demonstrated that$\text{BREAD}/\text{BREAD}++$is superior than existing index-free competitors: our methods provide not only fast but also accurate responses and they are of practical value to k-hop reachability feature extraction in the fraud detection system of WeChat. Our implementation is available at https://github.com/XMUDM/BREAD.