# Influence maximization in unknown social networks: Learning Policies for Effective Graph Sampling

arXiv: https://arxiv.org/abs/1907.11625 · HTML: https://mlsanity.com/paper/1907.11625/

## Key facts

- Cited by 20 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 8 Jul 2019.

## Built on by (most cited first, 20 of 20)

- [Deep Graph Representation Learning and Optimization for Influence Maximization](https://mlsanity.com/paper/2305.02200/) (2023)
- [A Survey on Influence Maximization: From an ML-Based Combinatorial Optimization](https://mlsanity.com/paper/2211.03074/) (2022)
- [Contingency-Aware Influence Maximization: A Reinforcement Learning Approach](https://mlsanity.com/paper/2106.07039/) (2021)
- [ToupleGDD: A Fine-Designed Solution of Influence Maximization by Deep Reinforcement Learning](https://mlsanity.com/paper/2210.07500/) (2022)
- [Adversarial Socialbots Modeling Based on Structural Information Principles](https://mlsanity.com/paper/2312.08098/) (2023)
- [CLAIM: Curriculum Learning Policy for Influence Maximization in Unknown Social Networks](https://mlsanity.com/paper/2107.03603/) (2021)
- [Active Screening for Recurrent Diseases: A Reinforcement Learning Approach](https://mlsanity.com/paper/2101.02766/) (2021)
- [IM-META: Influence Maximization Using Node Metadata in Networks With Unknown Topology](https://mlsanity.com/paper/2106.02926/) (2021)
- [Socialbots on Fire: Modeling Adversarial Behaviors of Socialbots via Multi-Agent Hierarchical Reinforcement Learning](https://mlsanity.com/paper/2110.10655/) (2021)
- [Towards A Multi-agent System for Online Hate Speech Detection](https://mlsanity.com/paper/2105.01129/) (2021)
- [Identifying Influential Users in Unknown Social Networks for Adaptive Incentive Allocation Under Budget Restriction](https://mlsanity.com/paper/2107.05992/) (2021)
- [Learning to Detect Critical Nodes in Sparse Graphs via Feature Importance Awareness](https://mlsanity.com/paper/2112.03404/) (2021)
- [GAC: A Deep Reinforcement Learning Model Toward User Incentivization in Unknown Social Networks](https://mlsanity.com/paper/2203.09578/) (2022)
- [META-CODE: Community Detection via Exploratory Learning in Topologically Unknown Networks](https://mlsanity.com/paper/2208.11015/) (2022)
- [Random Majority Opinion Diffusion: Stabilization Time, Absorbing States, and Influential Nodes](https://mlsanity.com/paper/2302.06760/) (2023)
- [Dyadic Reinforcement Learning](https://mlsanity.com/paper/2308.07843/) (2023)
- [Revealing Positive and Negative Role Models to Help People Make Good Decisions](https://mlsanity.com/paper/2603.02495/) (2026)
- [Frequent Itemset-driven Search for Finding Minimum Node Separators in Complex Networks](https://mlsanity.com/paper/2201.06877/) (2022)
- [A Unified Framework for Exploratory Learning-Aided Community Detection Under Topological Uncertainty](https://mlsanity.com/paper/2304.04497/) (2023)
- [A Benchmark Study of Deep-RL Methods for Maximum Coverage Problems over Graphs](https://mlsanity.com/paper/2406.14697/) (2024)

## Abstract (arXiv)

A serious challenge when finding influential actors in real-world social networks is the lack of knowledge about the structure of the underlying network. Current state-of-the-art methods rely on hand-crafted sampling algorithms; these methods sample nodes and their neighbours in a carefully constructed order and choose opinion leaders from this discovered network to maximize influence spread in the (unknown) complete network. In this work, we propose a reinforcement learning framework for network discovery that automatically learns useful node and graph representations that encode important structural properties of the network. At training time, the method identifies portions of the network such that the nodes selected from this sampled subgraph can effectively influence nodes in the complete network. The realization of such transferable network structure based adaptable policies is attributed to the meticulous design of the framework that encodes relevant node and graph signatures driven by an appropriate reward scheme. We experiment with real-world social networks from four different domains and show that the policies learned by our RL agent provide a 10-36% improvement over the current state-of-the-art method.