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تسجيل مستخدم جديدCLAIM: Curriculum Learning Policy for Influence Maximization in Unknown Social Networks
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Influence maximization is the problem of finding a small subset of nodes in a network that can maximize the diffusion of information. Recently, it has also found application in HIV prevention, substance abuse prevention, micro-finance adoption, etc., where the goal is to identify the set of peer leaders in a real-world physical social network who can disseminate information to a large group of people. Unlike online social networks, real-world networks are not completely known, and collecting information about the network is costly as it involves surveying multiple people. In this paper, we focus on this problem of network discovery for influence maximization. The existing work in this direction proposes a reinforcement learning framework. As the environment interactions in real-world settings are costly, so it is important for the reinforcement learning algorithms to have minimum possible environment interactions, i.e, to be sample efficient. In this work, we propose CLAIM - Curriculum LeArning Policy for Influence Maximization to improve the sample efficiency of RL methods. We conduct experiments on real-world datasets and show that our approach can outperform the current best approach.
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Influence maximization is the task of finding the smallest set of nodes whose activation in a social network can trigger an activation cascade that reaches the targeted network coverage, where threshold rules determine the outcome of influence. This
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Influence maximization (IM) aims at maximizing the spread of influence by offering discounts to influential users (called seeding). In many applications, due to users privacy concern, overwhelming network scale etc., it is hard to target any user in
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