# Fractional Budget Allocation for Influence Maximization under General Marketing Strategies

arXiv: https://arxiv.org/abs/2407.05669 · HTML: https://mlsanity.com/paper/2407.05669/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 3 of its 28 references that are in the map.
- First posted on arXiv 8 Jul 2024.

## Builds on (most cited first, 3 of 3)

- [Characteristic Functions on Graphs: Birds of a Feather, from Statistical Descriptors to Parametric Models](https://mlsanity.com/paper/2005.07959/) (2020)
- [A Community-Aware Framework for Social Influence Maximization](https://mlsanity.com/paper/2207.08937/) (2022)
- [CyNetDiff -- A Python Library for Accelerated Implementation of Network Diffusion Models](https://mlsanity.com/paper/2404.17059/) (2024)

## Built on by (most cited first, 4 of 4)

- [A Pressure-Based Diffusion Model for Influence Maximization on Social Networks](https://mlsanity.com/paper/2509.12822/) (2025)
- [A Community-Aware Framework for Influence Maximization with Explicit Accounting for Inter-Community Influence](https://mlsanity.com/paper/2512.23973/) (2025)
- [SARC: Sentiment-Augmented Deep Role Clustering for Fake News Detection](https://mlsanity.com/paper/2511.04692/) (2025)
- [LOFA: Online Influence Maximization under Full-Bandit Feedback using Lazy Forward Selection](https://mlsanity.com/paper/2601.00933/) (2026)

## Abstract (arXiv)

We consider the fractional influence maximization problem, i.e., identifying users on a social network to be incentivized with potentially partial discounts to maximize the influence on the network. The larger the discount given to a user, the higher the likelihood of its activation (adopting a new product or innovation), who then attempts to activate its neighboring users, causing a cascade effect of influence through the network. Our goal is to devise efficient algorithms that assign initial discounts to the network's users to maximize the total number of activated users at the end of the cascade, subject to a constraint on the total sum of discounts given. In general, the activation likelihood could be any non-decreasing function of the discount, whereas, our focus lies on the case when the activation likelihood is an affine function of the discount, potentially varying across different users. As this problem is shown to be NP-hard, we propose and analyze an efficient (1-1/e)-approximation algorithm. Furthermore, we run experiments on real-world social networks to show the performance and scalability of our method.