# Deep Graph Representation Learning and Optimization for Influence Maximization

arXiv: https://arxiv.org/abs/2305.02200 · HTML: https://mlsanity.com/paper/2305.02200/

## Key facts

- Cited by 27 papers in the map (data through 14 Oct 2026).
- Builds on 18 of its 53 references that are in the map.
- First posted on arXiv 1 May 2023.

## Builds on (most cited first, 18 of 18)

- [Semi-Supervised Classification with Graph Convolutional Networks](https://mlsanity.com/paper/1609.02907/) (2016)
- [Graph Attention Networks](https://mlsanity.com/paper/1710.10903/) (2017)
- [How Powerful are Graph Neural Networks?](https://mlsanity.com/paper/1810.00826/) (2018)
- [A Comprehensive Survey on Graph Neural Networks](https://mlsanity.com/paper/1901.00596/) (2019)
- [GRAND: Graph Neural Diffusion](https://mlsanity.com/paper/2106.10934/) (2021)
- [Influence Maximization: Near-Optimal Time Complexity Meets Practical Efficiency](https://mlsanity.com/paper/1404.0900/) (2014)
- [Influence maximization in unknown social networks: Learning Policies for Effective Graph Sampling](https://mlsanity.com/paper/1907.11625/) (2019)
- [Source Localization of Graph Diffusion via Variational Autoencoders for Graph Inverse Problems](https://mlsanity.com/paper/2206.12327/) (2022)
- [Online Influence Maximization (Extended Version)](https://mlsanity.com/paper/1506.01188/) (2015)
- [Stop-and-Stare: Optimal Sampling Algorithms for Viral Marketing in Billion-scale Networks](https://mlsanity.com/paper/1605.07990/) (2016)
- [An Invertible Graph Diffusion Neural Network for Source Localization](https://mlsanity.com/paper/2206.09214/) (2022)
- [A Survey on Influence Maximization in a Social Network](https://mlsanity.com/paper/1808.05502/) (2018)
- [DISCO: Influence Maximization Meets Network Embedding and Deep Learning](https://mlsanity.com/paper/1906.07378/) (2019)
- [ToupleGDD: A Fine-Designed Solution of Influence Maximization by Deep Reinforcement Learning](https://mlsanity.com/paper/2210.07500/) (2022)
- [Deep Generation of Heterogeneous Networks](https://mlsanity.com/paper/2206.12336/) (2022)
- [DeepGAR: Deep Graph Learning for Analogical Reasoning](https://mlsanity.com/paper/2211.10821/) (2022)
- [Boosting Information Spread: An Algorithmic Approach](https://mlsanity.com/paper/1602.03111/) (2016)
- [Real-Time Influence Maximization on Dynamic Social Streams](https://mlsanity.com/paper/1702.01586/) (2017)

## Built on by (most cited first, 27 of 27)

- [Beyond Text: A Deep Dive into Large Language Models' Ability on Understanding Graph Data](https://mlsanity.com/paper/2310.04944/) (2023)
- [Curriculum Learning for Graph Neural Networks: Which Edges Should We Learn First](https://mlsanity.com/paper/2310.18735/) (2023)
- [Depth-Adaptive Graph Neural Networks via Learnable Bakry-'Emery Curvature](https://mlsanity.com/paper/2503.01079/) (2025)
- [Staleness-Alleviated Distributed GNN Training via Online Dynamic-Embedding Prediction](https://mlsanity.com/paper/2308.13466/) (2023)
- [Graph Bayesian Optimization for Multiplex Influence Maximization](https://mlsanity.com/paper/2403.18866/) (2024)
- [DeepSN: A Sheaf Neural Framework for Influence Maximization](https://mlsanity.com/paper/2412.12416/) (2024)
- [Fast Inference of Removal-Based Node Influence](https://mlsanity.com/paper/2403.08333/) (2024)
- [Identifying Super Spreaders in Multilayer Networks](https://mlsanity.com/paper/2505.20980/) (2025)
- [Critical Nodes Identification in Complex Networks: A Survey](https://mlsanity.com/paper/2507.06164/) (2025)
- [MIM-Reasoner: Learning with Theoretical Guarantees for Multiplex Influence Maximization](https://mlsanity.com/paper/2402.16898/) (2024)
- [Many-Objective Evolutionary Influence Maximization: Balancing Spread, Budget, Fairness, and Time](https://mlsanity.com/paper/2403.18755/) (2024)
- [Representation Learning of Geometric Trees](https://mlsanity.com/paper/2408.08799/) (2024)
- [Graph Neural Diffusion via Generalized Opinion Dynamics](https://mlsanity.com/paper/2508.11249/) (2025)
- [Online Influence Maximization: Concept and Algorithm](https://mlsanity.com/paper/2312.00099/) (2023)
- [Non-Euclidean Spatial Graph Neural Network](https://mlsanity.com/paper/2312.10808/) (2023)
- [Non-Progressive Influence Maximization in Dynamic Social Networks](https://mlsanity.com/paper/2412.07402/) (2024)
- [Influence Strength Estimation in Hyperbolic Space for Social Influence Maximization](https://mlsanity.com/paper/2502.13571/) (2025)
- [Vulnerable Agent Identification in Large-Scale Multi-Agent Reinforcement Learning](https://mlsanity.com/paper/2509.15103/) (2025)
- [Bridging Computational Social Science and Deep Learning: Cultural Dissemination-Inspired Graph Neural Networks](https://mlsanity.com/paper/2509.19084/) (2025)
- [PACIFIER: Pacing Opinion Depolarization via a Unified Graph Learning Framework](https://mlsanity.com/paper/2602.23390/) (2026)
- [Invariant-Stratified Propagation for Expressive Graph Neural Networks](https://mlsanity.com/paper/2603.01388/) (2026)
- [SP-GCRL: Influence Maximization on Incomplete Social Graphs](https://mlsanity.com/paper/2605.12513/) (2026)
- [Towards Graph Foundation Models for Dynamics in Complex Networked Systems: Lessons from Super-Spreader Identification in Multilayer Networks](https://mlsanity.com/paper/2606.08306/) (2026)
- [Graph Reduction in Multirelational Networks: A Spreading-Oriented Reduction Benchmark](https://mlsanity.com/paper/2606.12581/) (2026)
- [Placing Degree Scales After LayerNorm](https://mlsanity.com/paper/2606.14022/) (2026)
- [Rethinking Learning-Based Influence Maximization: Simple Neural Surrogates and Native Discrete Search](https://mlsanity.com/paper/2608.08406/) (2026)
- [Network World Models as Environments for Algorithm Design on Complex Systems](https://mlsanity.com/paper/2610.01048/) (2026)

## Abstract (arXiv)

Influence maximization (IM) is formulated as selecting a set of initial users from a social network to maximize the expected number of influenced users. Researchers have made great progress in designing various traditional methods, and their theoretical design and performance gain are close to a limit. In the past few years, learning-based IM methods have emerged to achieve stronger generalization ability to unknown graphs than traditional ones. However, the development of learning-based IM methods is still limited by fundamental obstacles, including 1) the difficulty of effectively solving the objective function; 2) the difficulty of characterizing the diversified underlying diffusion patterns; and 3) the difficulty of adapting the solution under various node-centrality-constrained IM variants. To cope with the above challenges, we design a novel framework DeepIM to generatively characterize the latent representation of seed sets, and we propose to learn the diversified information diffusion pattern in a data-driven and end-to-end manner. Finally, we design a novel objective function to infer optimal seed sets under flexible node-centrality-based budget constraints. Extensive analyses are conducted over both synthetic and real-world datasets to demonstrate the overall performance of DeepIM. The code and data are available at: https://github.com/triplej0079/DeepIM.