# Differentially Describing Groups of Graphs

arXiv: https://arxiv.org/abs/2201.04064 · HTML: https://mlsanity.com/paper/2201.04064/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 16 of its 49 references that are in the map.
- First posted on arXiv 16 Dec 2021.

## Builds on (most cited first, 16 of 16)

- [Explainable Classification of Brain Networks via Contrast Subgraphs](https://mlsanity.com/paper/2006.05176/) (2020)
- [Hypothesis Testing For Network Data in Functional Neuroimaging](https://mlsanity.com/paper/1407.5525/) (2014)
- [Graph Summarization Methods and Applications: A Survey](https://mlsanity.com/paper/1612.04883/) (2016)
- [Two-sample Hypothesis Testing for Inhomogeneous Random Graphs](https://mlsanity.com/paper/1707.00833/) (2017)
- [Modeling Network Populations via Graph Distances](https://mlsanity.com/paper/1904.07367/) (2019)
- [Nonparametric Bayes Modeling of Populations of Networks](https://mlsanity.com/paper/1406.7851/) (2014)
- [On clustering network-valued data](https://mlsanity.com/paper/1606.02401/) (2016)
- [Fast and Memory-Efficient Significant Pattern Mining via Permutation Testing](https://mlsanity.com/paper/1502.04315/) (2015)
- [Bayesian Inference and Testing of Group Differences in Brain Networks](https://mlsanity.com/paper/1411.6506/) (2014)
- [Bayesian Joint Modeling of Multiple Brain Functional Networks](https://mlsanity.com/paper/1708.02123/) (2017)
- [Statistical inference for network samples using subgraph counts](https://mlsanity.com/paper/1701.00505/) (2017)
- [Model-based clustering for populations of networks](https://mlsanity.com/paper/1806.00225/) (2018)
- [Graph Similarity Description: How Are These Graphs Similar?](https://mlsanity.com/paper/2105.14364/) (2021)
- [Summarizing Data Succinctly with the Most Informative Itemsets](https://mlsanity.com/paper/1904.11134/) (2019)
- [Multi-level hypothesis testing for populations of heterogeneous networks](https://mlsanity.com/paper/1809.02512/) (2018)
- [Integrative Learning for Population of Dynamic Networks with Covariates](https://mlsanity.com/paper/2101.05539/) (2021)

## Built on by (most cited first, 1 of 1)

- [Scalable inference of spatial regions and temporal signatures from time series](https://mlsanity.com/paper/2605.05008/) (2026)

## Abstract (arXiv)

How does neural connectivity in autistic children differ from neural connectivity in healthy children or autistic youths? What patterns in global trade networks are shared across classes of goods, and how do these patterns change over time? Answering questions like these requires us to differentially describe groups of graphs: Given a set of graphs and a partition of these graphs into groups, discover what graphs in one group have in common, how they systematically differ from graphs in other groups, and how multiple groups of graphs are related. We refer to this task as graph group analysis, which seeks to describe similarities and differences between graph groups by means of statistically significant subgraphs. To perform graph group analysis, we introduce Gragra, which uses maximum entropy modeling to identify a non-redundant set of subgraphs with statistically significant associations to one or more graph groups. Through an extensive set of experiments on a wide range of synthetic and real-world graph groups, we confirm that Gragra works well in practice.