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Dynamic Gradient Influencing for Viral Marketing Using Graph Neural Networks

arXiv 2403.12399 ↗First posted 19 Mar 2024cs.LG cs.CR cs.SIcited by 0

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Abstract (arXiv)

The problem of maximizing the adoption of a product through viral marketing in social networks has been studied heavily through postulated network models. We present a novel data-driven formulation of the problem. We use Graph Neural Networks (GNNs) to model the adoption of products by utilizing both topological and attribute information. The resulting Dynamic Viral Marketing (DVM) problem seeks to find the minimum budget and minimal set of dynamic topological and attribute changes in order to attain a specified adoption goal. We show that DVM is NP-Hard and is related to the existing influence maximization problem. Motivated by this connection, we develop the idea of Dynamic Gradient Influencing (DGI) that uses gradient ranking to find optimal perturbations and targets low-budget and high influence non-adopters in discrete steps. We use an efficient strategy for computing node budgets and develop the ''Meta-Influence'' heuristic for assessing a node's downstream influence. We evaluate DGI against multiple baselines and demonstrate gains on average of 24% on budget and 37% on AUC on real-world attributed networks. Our code is publicly available at https://github.com/saurabhsharma1993/dynamic_viral_marketing.

Key facts. No citing papers in the map yet (data through 7 Oct 2026). Builds on 22 of its 37 references that are in the map. First posted on arXiv 19 Mar 2024.

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22 of 37 references.

References · 22

  1. 2016Semi-Supervised Classification with Graph Convolutional Networkscited by 11,282
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  10. 2019Measuring and Relieving the Over-smoothing Problem for Graph Neural Networks from the Topological Viewcited by 544
  11. 2018Efficient Neural Network Robustness Certification with General Activation Functionscited by 439
  12. 2018Adversarial Attack on Graph Structured Datacited by 300
10 more
  1. 2019Adversarial Attacks on Graph Neural Networks via Meta Learningcited by 261
  2. 2019Topology Attack and Defense for Graph Neural Networks: An Optimization Perspectivecited by 196
  3. 2021Robustness of Graph Neural Networks at Scalecited by 100
  4. 2020Unifying Graph Convolutional Neural Networks and Label Propagationcited by 98
  5. 2020DeepRobust: A PyTorch Library for Adversarial Attacks and Defensescited by 78
  6. 2020Model-based Reinforcement Learning: A Surveycited by 39
  7. 2023Are Defenses for Graph Neural Networks Robust?cited by 38
  8. 2015Exploring the Space of Adversarial Imagescited by 28
  9. 2024Minimum Topology Attacks for Graph Neural Networkscited by 6
  10. 2023Opinion-aware Influence Maximization in Online Social Networkscited by 1

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