# DANI: Fast Diffusion Aware Network Inference with Preserving Topological Structure Property

arXiv: https://arxiv.org/abs/2310.01696 · HTML: https://mlsanity.com/paper/2310.01696/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 11 of its 50 references that are in the map.
- First posted on arXiv 2 Oct 2023.

## Builds on (most cited first, 11 of 11)

- [SNAP: A General Purpose Network Analysis and Graph Mining Library](https://mlsanity.com/paper/1606.07550/) (2016)
- [A Survey of Information Cascade Analysis: Models, Predictions, and Recent Advances](https://mlsanity.com/paper/2005.11041/) (2020)
- [Virality Prediction and Community Structure in Social Networks](https://mlsanity.com/paper/1306.0158/) (2013)
- [Bayesian inference of network structure from information cascades](https://mlsanity.com/paper/1908.03318/) (2019)
- [Capturing Dynamics of Information Diffusion in SNS: A Survey of Methodology and Techniques](https://mlsanity.com/paper/2110.14245/) (2021)
- [Network Structure Inference, A Survey: Motivations, Methods, and Applications](https://mlsanity.com/paper/1610.00782/) (2016)
- [DANI: A Fast Diffusion Aware Network Inference Algorithm](https://mlsanity.com/paper/1706.00941/) (2017)
- [Interest Clustering Coefficient: a New Metric for Directed Networks like Twitter](https://mlsanity.com/paper/2008.00517/) (2020)
- [Phantom cascades: The effect of hidden nodes on information diffusion](https://mlsanity.com/paper/1502.01602/) (2015)
- [Bayesian Overlapping Community Detection in Dynamic Networks](https://mlsanity.com/paper/1605.02288/) (2016)
- [LazyFox: Fast and parallelized overlapping community detection in large graphs](https://mlsanity.com/paper/2210.03211/) (2022)

## Abstract (arXiv)

The fast growth of social networks and their data access limitations in recent years has led to increasing difficulty in obtaining the complete topology of these networks. However, diffusion information over these networks is available, and many algorithms have been proposed to infer the underlying networks using this information. The previously proposed algorithms only focus on inferring more links and ignore preserving the critical topological characteristics of the underlying social networks. In this paper, we propose a novel method called DANI to infer the underlying network while preserving its structural properties. It is based on the Markov transition matrix derived from time series cascades, as well as the node-node similarity that can be observed in the cascade behavior from a structural point of view. In addition, the presented method has linear time complexity (increases linearly with the number of nodes, number of cascades, and square of the average length of cascades), and its distributed version in the MapReduce framework is also scalable. We applied the proposed approach to both real and synthetic networks. The experimental results showed that DANI has higher accuracy and lower run time while maintaining structural properties, including modular structure, degree distribution, connected components, density, and clustering coefficients, than well-known network inference methods.