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In many real-world applications such as social network analysis and online advertising/marketing, one of the most important and popular problems is called influence maximization (IM), which finds a set of k seed users that maximize the expected number of influenced user nodes. In practice, however, maximizing the number of influenced nodes may be far from satisfactory for real applications such as opinion promotion and collective buying. In this paper, we explore the importance of stability and triangles in social networks, and formulate a novel problem in the influence spread scenario, named triangular stability maximization , over social networks, and generalize it to a general triangle influence maximization problem, which is proved to be NP-hard. We develop an efficient reverse influence sampling (RIS) based framework for the triangle IM with theoretical guarantees. To enable unbiased estimators, it demands probabilistic sampling of triangles, that is, sampling triangles according to their probabilities. We propose an edge-based triple sampling approach, which is exactly equivalent to probabilistic sampling and avoids costly triangle enumeration and materialization. We also design several pruning and reduction techniques, as well as a cost-model-guided heuristic algorithm. Extensive experiments and a case study over real-world graphs confirm the effectiveness of our proposed algorithms and the superiority of triangular stability maximization and triangle influence maximization. 

Due to repetitive trial-and-error style interactions between agents and a fixed traffic environment during the policy learning, existing Reinforcement Learning (RL)-based Traffic Signal Control (TSC) methods greatly suffer from long RL training time and poor adaptability of RL agents to other complex traffic environments. To address these problems, we propose a novel Adversarial Inverse Reinforcement Learning (AIRL)-based pre-training method named InitLight, which enables effective initial model generation for TSC agents. Unlike traditional RL-based TSC approaches that train a large number of agents simultaneously for a specific multi-intersection environment, InitLight pretrains only one single initial model based on multiple single-intersection environments together with their expert trajectories. Since the reward function learned by InitLight can recover ground-truth TSC rewards for different intersections at optimality, the pre-trained agent can be deployed at intersections of any traffic environments as initial models to accelerate subsequent overall global RL training. Comprehensive experimental results show that, the initial model generated by InitLight can not only significantly accelerate the convergence with much fewer episodes, but also own superior generalization ability to accommodate various kinds of complex traffic environments.