text: 158,246,1404624618695049218,1148625407073247233,Haipeng Chen,"Glad to share our recent UAI-2021 paper @UncertaintyInAI , titled ""Contingency-Aware Influence Maximization: A Reinforcement Learning Approach."" We propose to address the contingency-aware influence maximization (CAIM) problem from a RL perspective. <LINK> CAIM is motivated by a series of studies in our group with @MilindTambe_AI, on HIV prevention among homeless youth social networks in the city of LA. Different from normal IM problems, there is uncertainty in a node's willingness to be seeds. (image credit: Wilder et al.) <LINK> Despite recent success in the field using POMDPs or greedy algorithms (Yadav et al. 2016, Wilder et al. 2018, 2021), a main limitation of scaling their methods to more homeless youth shelters is the large run time, a big burden to NGOs who don't have HPC resources. Inspired by recent work that uses RL to address combinatorial optimization problems, we extend it to the CAIM problem. We show the extension is computationally hard due to the uncertainty of a node's willingness, and propose a new reward shaping technique. The reward shaping component is proven to be computationally efficient both in theory (via the submodularity property of the influence cascade models) and in empirical evaluation (via an ablation study). Main takeaway -- our algorithm RL4IM achieves comparable influence to SOTA algorithm for CAIM, while requiring negligible runtime during test phase, which can be directly used by NGOs - a result making RL4IM an ideal alternative for CAIM in the low-resource computing paradigm. Our work is an example of how RL can be used for social good work, and we hope to encourage more AI researchers in exploring the idea here to assist AI for social good in the low-resource computing domain. Join work with @WeiQiu9, @HanChingOu, Bo An, and @MilindTambe_AI Recent works on HIV prevention among homeless youth networks using influence maximization, with success in field test, by @brwilder @AmulyaYadav19 @MilindTambe_AI @EricRicePhD Work that combines graph representation learning and reinforcement learning to address combinatorial optimization problems, by @lyeskhalil et al. 2017",https://arxiv.org/abs/2106.07039,"The influence maximization (IM) problem aims at finding a subset of seed nodes in a social network that maximize the spread of influence. In this study, we focus on a sub-class of IM problems, where whether the nodes are willing to be the seeds when being invited is uncertain, called contingency-aware IM. Such contingency aware IM is critical for applications for non-profit organizations in low resource communities (e.g., spreading awareness of disease prevention). Despite the initial success, a major practical obstacle in promoting the solutions to more communities is the tremendous runtime of the greedy algorithms and the lack of high performance computing (HPC) for the non-profits in the field -- whenever there is a new social network, the non-profits usually do not have the HPCs to recalculate the solutions. Motivated by this and inspired by the line of works that use reinforcement learning (RL) to address combinatorial optimization on graphs, we formalize the problem as a Markov Decision Process (MDP), and use RL to learn an IM policy over historically seen networks, and generalize to unseen networks with negligible runtime at test phase. To fully exploit the properties of our targeted problem, we propose two technical innovations that improve the existing methods, including state-abstraction and theoretically grounded reward shaping. Empirical results show that our method achieves influence as high as the state-of-the-art methods for contingency-aware IM, while having negligible runtime at test phase. ","Contingency-Aware Influence Maximization: A Reinforcement Learning