# GraMeR: Graph Meta Reinforcement Learning for Multi-Objective Influence Maximization

arXiv: https://arxiv.org/abs/2205.14834 · HTML: https://mlsanity.com/paper/2205.14834/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 13 of its 46 references that are in the map.
- First posted on arXiv 30 May 2022.

## Builds on (most cited first, 13 of 13)

- [Semi-Supervised Classification with Graph Convolutional Networks](https://mlsanity.com/paper/1609.02907/) (2016)
- [Inductive Representation Learning on Large Graphs](https://mlsanity.com/paper/1706.02216/) (2017)
- [Pointer Networks](https://mlsanity.com/paper/1506.03134/) (2015)
- [Learning Combinatorial Optimization Algorithms over Graphs](https://mlsanity.com/paper/1704.01665/) (2017)
- [Combinatorial Optimization with Graph Convolutional Networks and Guided Tree Search](https://mlsanity.com/paper/1810.10659/) (2018)
- [A Multi-Objective Deep Reinforcement Learning Framework](https://mlsanity.com/paper/1803.02965/) (2018)
- [Shaping Social Activity by Incentivizing Users](https://mlsanity.com/paper/1408.0406/) (2014)
- [A General Framework for quantifying Aleatoric and Epistemic uncertainty in Graph Neural Networks](https://mlsanity.com/paper/2205.09968/) (2022)
- [Scalable Graph Neural Network-based framework for identifying critical nodes and links in Complex Networks](https://mlsanity.com/paper/2012.15725/) (2020)
- [Opinion Maximization in Social Networks](https://mlsanity.com/paper/1301.7455/) (2013)
- [Bayesian Graph Neural Network for Fast identification of critical nodes in Uncertain Complex Networks](https://mlsanity.com/paper/2012.15733/) (2020)
- [Exploring the Role of Intrinsic Nodal Activation on the Spread of Influence in Complex Networks](https://mlsanity.com/paper/1707.05287/) (2017)
- [Robustness Assessment of Hetero-Functional Graph Theory Based Model of Interdependent Urban Utility Networks](https://mlsanity.com/paper/2008.11831/) (2020)

## Built on by (most cited first, 2 of 2)

- [Challenges and Opportunities in Deep Reinforcement Learning with Graph Neural Networks: A Comprehensive review of Algorithms and Applications](https://mlsanity.com/paper/2206.07922/) (2022)
- [GNN-Based Candidate Node Predictor for Influence Maximization in Temporal Graphs](https://mlsanity.com/paper/2503.23713/) (2025)

## Abstract (arXiv)

Influence maximization (IM) is a combinatorial problem of identifying a subset of nodes called the seed nodes in a network (graph), which when activated, provide a maximal spread of influence in the network for a given diffusion model and a budget for seed set size. IM has numerous applications such as viral marketing, epidemic control, sensor placement and other network-related tasks. However, the uses are limited due to the computational complexity of current algorithms. Recently, learning heuristics for IM have been explored to ease the computational burden. However, there are serious limitations in current approaches such as: (1) IM formulations only consider influence via spread and ignore self activation; (2) scalability to large graphs; (3) generalizability across graph families; (4) low computational efficiency with a large running time to identify seed sets for every test network. In this work, we address each of these limitations through a unique approach that involves (1) formulating a generic IM problem as a Markov decision process that handles both intrinsic and influence activations; (2) employing double Q learning to estimate seed nodes; (3) ensuring scalability via sub-graph based representations; and (4) incorporating generalizability via meta-learning across graph families. Extensive experiments are carried out in various standard networks to validate performance of the proposed Graph Meta Reinforcement learning (GraMeR) framework. The results indicate that GraMeR is multiple orders faster and generic than conventional approaches.