# Fair Information Spread on Social Networks with Community Structure

arXiv: https://arxiv.org/abs/2305.08791 · HTML: https://mlsanity.com/paper/2305.08791/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 8 of its 28 references that are in the map.
- First posted on arXiv 15 May 2023.

## Builds on (most cited first, 8 of 8)

- [Community Detection and Stochastic Block Models](https://mlsanity.com/paper/1703.10146/) (2017)
- [Fairness in Machine Learning](https://mlsanity.com/paper/2012.15816/) (2020)
- [Network Cross-Validation for Determining the Number of Communities in Network Data](https://mlsanity.com/paper/1411.1715/) (2014)
- [Group-Fairness in Influence Maximization](https://mlsanity.com/paper/1903.00967/) (2019)
- [A Survey on Influence Maximization in a Social Network](https://mlsanity.com/paper/1808.05502/) (2018)
- [On the Fairness of Time-Critical Influence Maximization in Social Networks](https://mlsanity.com/paper/1905.06618/) (2019)
- [Fairness in Influence Maximization through Randomization](https://mlsanity.com/paper/2010.03438/) (2020)
- [Overlapping community detection in networks via sparse spectral decomposition](https://mlsanity.com/paper/2009.10641/) (2020)

## Abstract (arXiv)

Information spread through social networks is ubiquitous. Influence maximiza- tion (IM) algorithms aim to identify individuals who will generate the greatest spread through the social network if provided with information, and have been largely devel- oped with marketing in mind. In social networks with community structure, which are very common, IM algorithms focused solely on maximizing spread may yield signifi- cant disparities in information coverage between communities, which is problematic in settings such as public health messaging. While some IM algorithms aim to remedy disparity in information coverage using node attributes, none use the empirical com- munity structure within the network itself, which may be beneficial since communities directly affect the spread of information. Further, the use of empirical network struc- ture allows us to leverage community detection techniques, making it possible to run fair-aware algorithms when there are no relevant node attributes available, or when node attributes do not accurately capture network community structure. In contrast to other fair IM algorithms, this work relies on fitting a model to the social network which is then used to determine a seed allocation strategy for optimal fair information spread. We develop an algorithm to determine optimal seed allocations for expected fair coverage, defined through maximum entropy, provide some theoretical guarantees under appropriate conditions, and demonstrate its empirical accuracy on both simu- lated and real networks. Because this algorithm relies on a fitted network model and not on the network directly, it is well-suited for partially observed and noisy social networks.