# CLAIM: Curriculum Learning Policy for Influence Maximization in Unknown Social Networks

arXiv: https://arxiv.org/abs/2107.03603 · HTML: https://mlsanity.com/paper/2107.03603/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- Builds on 10 of its 26 references that are in the map.
- First posted on arXiv 8 Jul 2021.

## Builds on (most cited first, 10 of 10)

- [Hindsight Experience Replay](https://mlsanity.com/paper/1707.01495/) (2017)
- [Hierarchical Graph Representation Learning with Differentiable Pooling](https://mlsanity.com/paper/1806.08804/) (2018)
- [Large-Scale Study of Curiosity-Driven Learning](https://mlsanity.com/paper/1808.04355/) (2018)
- [Visual Reinforcement Learning with Imagined Goals](https://mlsanity.com/paper/1807.04742/) (2018)
- [Intrinsic Motivation and Automatic Curricula via Asymmetric Self-Play](https://mlsanity.com/paper/1703.05407/) (2017)
- [CURIOUS: Intrinsically Motivated Modular Multi-Goal Reinforcement Learning](https://mlsanity.com/paper/1810.06284/) (2018)
- [Influence maximization in unknown social networks: Learning Policies for Effective Graph Sampling](https://mlsanity.com/paper/1907.11625/) (2019)
- [Using Social Networks to Aid Homeless Shelters: Dynamic Influence Maximization under Uncertainty - An Extended Version](https://mlsanity.com/paper/1602.00165/) (2016)
- [Robust Influence Maximization](https://mlsanity.com/paper/1601.06551/) (2016)
- [NetworkRepository: An Interactive Data Repository with Multi-scale Visual Analytics](https://mlsanity.com/paper/1410.3560/) (2014)

## Built on by (most cited first, 7 of 7)

- [A Survey on Influence Maximization: From an ML-Based Combinatorial Optimization](https://mlsanity.com/paper/2211.03074/) (2022)
- [Efficient Resource Allocation with Fairness Constraints in Restless Multi-Armed Bandits](https://mlsanity.com/paper/2206.03883/) (2022)
- [Generative Modelling of Stochastic Actions with Arbitrary Constraints in Reinforcement Learning](https://mlsanity.com/paper/2311.15341/) (2023)
- [Adversarial Socialbots Modeling Based on Structural Information Principles](https://mlsanity.com/paper/2312.08098/) (2023)
- [Socialbots on Fire: Modeling Adversarial Behaviors of Socialbots via Multi-Agent Hierarchical Reinforcement Learning](https://mlsanity.com/paper/2110.10655/) (2021)
- [Dyadic Reinforcement Learning](https://mlsanity.com/paper/2308.07843/) (2023)
- [Diversity Induced Environment Design via Self-Play](https://mlsanity.com/paper/2302.02119/) (2023)

## Abstract (arXiv)

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.