# Scalable Influence Estimation in Continuous-Time Diffusion Networks

arXiv: https://arxiv.org/abs/1311.3669 · HTML: https://mlsanity.com/paper/1311.3669/

## Key facts

- Cited by 8 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 14 Nov 2013.

## Built on by (most cited first, 8 of 8)

- [Network Inference and Influence Maximization from Samples](https://mlsanity.com/paper/2106.03403/) (2021)
- [Scalable Continuous-time Diffusion Framework for Network Inference and Influence Estimation](https://mlsanity.com/paper/2403.02867/) (2024)
- [Influence Maximization via Graph Neural Bandits](https://mlsanity.com/paper/2406.12835/) (2024)
- [Learning of networked spreading models from noisy and incomplete data](https://mlsanity.com/paper/2401.00011/) (2023)
- [Online Influence Maximization under the Independent Cascade Model with Node-Level Feedback](https://mlsanity.com/paper/2109.06077/) (2021)
- [Social-Inverse: Inverse Decision-making of Social Contagion Management with Task Migrations](https://mlsanity.com/paper/2209.10493/) (2022)
- [Influence Estimation and Maximization via Neural Mean-Field Dynamics](https://mlsanity.com/paper/2106.02608/) (2021)
- [Inferring Diffusion Structures of Heterogeneous Network Cascade](https://mlsanity.com/paper/2506.19142/) (2025)

## Abstract (arXiv)

If a piece of information is released from a media site, can it spread, in 1 month, to a million web pages? This influence estimation problem is very challenging since both the time-sensitive nature of the problem and the issue of scalability need to be addressed simultaneously. In this paper, we propose a randomized algorithm for influence estimation in continuous-time diffusion networks. Our algorithm can estimate the influence of every node in a network with |V| nodes and |E| edges to an accuracy of varepsilon using n=O(1/varepsilon2) randomizations and up to logarithmic factors O(n|E|+n|V|) computations. When used as a subroutine in a greedy influence maximization algorithm, our proposed method is guaranteed to find a set of nodes with an influence of at least (1-1/e)OPT-2varepsilon, where OPT is the optimal value. Experiments on both synthetic and real-world data show that the proposed method can easily scale up to networks of millions of nodes while significantly improves over previous state-of-the-arts in terms of the accuracy of the estimated influence and the quality of the selected nodes in maximizing the influence.