# Scalable Continuous-time Diffusion Framework for Network Inference and Influence Estimation

arXiv: https://arxiv.org/abs/2403.02867 · HTML: https://mlsanity.com/paper/2403.02867/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 9 of its 45 references that are in the map.
- First posted on arXiv 5 Mar 2024.

## Builds on (most cited first, 9 of 9)

- [DeepInf: Social Influence Prediction with Deep Learning](https://mlsanity.com/paper/1807.05560/) (2018)
- [Interpretable Sequence Learning for COVID-19 Forecasting](https://mlsanity.com/paper/2008.00646/) (2020)
- [Influence maximization in complex networks through optimal percolation](https://mlsanity.com/paper/1506.08326/) (2015)
- [Curating a COVID-19 data repository and forecasting county-level death counts in the United States](https://mlsanity.com/paper/2005.07882/) (2020)
- [Modeling Information Propagation with Survival Theory](https://mlsanity.com/paper/1305.3616/) (2013)
- [The Simple Rules of Social Contagion](https://mlsanity.com/paper/1308.5015/) (2013)
- [Scalable Influence Estimation in Continuous-Time Diffusion Networks](https://mlsanity.com/paper/1311.3669/) (2013)
- [Network Diffusions via Neural Mean-Field Dynamics](https://mlsanity.com/paper/2006.09449/) (2020)
- [Efficient Approximation Algorithms for Adaptive Influence Maximization](https://mlsanity.com/paper/2004.06469/) (2020)

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

- [AdaFisher: Adaptive Second Order Optimization via Fisher Information](https://mlsanity.com/paper/2405.16397/) (2024)
- [HATS: Hardness-Aware Trajectory Synthesis for GUI Agents](https://mlsanity.com/paper/2603.12138/) (2026)
- [Influence Strength Estimation in Hyperbolic Space for Social Influence Maximization](https://mlsanity.com/paper/2502.13571/) (2025)
- [A Unified Perspective on Adversarial Membership Manipulation in Vision Models](https://mlsanity.com/paper/2604.02780/) (2026)

## Abstract (arXiv)

The study of continuous-time information diffusion has been an important area of research for many applications in recent years. When only the diffusion traces (cascades) are accessible, cascade-based network inference and influence estimation are two essential problems to explore. Alas, existing methods exhibit limited capability to infer and process networks with more than a few thousand nodes, suffering from scalability issues. In this paper, we view the diffusion process as a continuous-time dynamical system, based on which we establish a continuous-time diffusion model. Subsequently, we instantiate the model to a scalable and effective framework (FIM) to approximate the diffusion propagation from available cascades, thereby inferring the underlying network structure. Furthermore, we undertake an analysis of the approximation error of FIM for network inference. To achieve the desired scalability for influence estimation, we devise an advanced sampling technique and significantly boost the efficiency. We also quantify the effect of the approximation error on influence estimation theoretically. Experimental results showcase the effectiveness and superior scalability of FIM on network inference and influence estimation.