Shortest distances estimation plays a crucial role in fields such as social network analysis, bioinformatics, and navigation systems. While the traditional Breadth First Search (BFS) algorithm is effective, it often incurs high computational costs when handling large datasets. Therefore, researches of labeling-based shortest distance estimation have been emerged, but there are still issues with insufficient accuracy and difficulty in controlling estimation errors. This paper introduces a method for constructing node coordinates based on peripheral node information called the Lighthouse-Coordinate (LC) Algorithm, which includes three components, Lighthouse Sampling (LS), Coordination Construction (CC), and Coordinate Distance Calculation (CDC).We first perform LS to collect candidate nodes for labelling as lighthouses for shortest distance estimation, then create the coordinates of all sampled lighthouses via CC based on their structural information, and finally estimate the approximate shortest distance by CDC using the constructed coordinates. It is worth mentioning that LC algorithm is an error controllable method, where users pre-define a maximum distance error E_max and LC algorithm returns an estimated shortest distance of two nodes ≤ E_max. We theoretically analyzed the estimated shortest distance is upper bounded by E_max. We conducted experiments on five real-world datasets and demonstrated an acceleration effect of one to three orders of magnitude, while also achieving controllable errors given the user-specific error bound.

Road pricing is an urban traffic management mechanism to reduce traffic congestion. Currently, most of the road pricing systems based on predefined charging tolls fail to consider the dynamics of urban traffic flows and travelers’ demands on the arrival time. In this paper, we propose a method to dynamically adjust online road toll based on traffic conditions and travelers’ demands to resolve the above-mentioned problems. The method, based on deep reinforcement learning, automatically allocates the optimal toll for each road during peak hours and guides vehicles to roads with lower toll charges. Moreover, it further considers travelers’ demands to ensure that more vehicles arrive at their destinations before their estimated arrival time. Our method can increase the traffic volume effectively, as compared to the existing static mechanisms.

Event temporal relation extraction is an important part of natural language processing. Many models are being used in this task with the development of deep learning. However, most of the existing methods cannot accurately obtain the degree of association between different tokens and events, and event-related information cannot be effectively integrated. In this paper, we propose an event information integration model that integrates event information through multilayer bidirectional long short-term memory (Bi-LSTM) and attention mechanism. Although the above scheme can improve the extraction performance, it can still be further optimized. To further improve the performance of the previous scheme, we propose a novel relational graph attention network that incorporates edge attributes. In this approach, we first build a semantic dependency graph through dependency parsing, model a semantic graph that considers the edges’ attributes by using top-k attention mechanisms to learn hidden semantic contextual representations, and finally predict event temporal relations. We evaluate proposed models on the TimeBank-Dense dataset. Compared to previous baselines, the Micro-F1 scores obtained by our models improve by 3.9% and 14.5%, respectively.