---
title: DINA Network Similarity Analysis
slug: dina-network-similarity-analysis
createdAt: 2026-04-28T11:04:24.453Z
---

To assess network similarity, we use DeltaCon ([1](docId\:Fx907VIxMfPwg4dpJ8-R5)), a graph comparison metric that quantifies structural similarity based on node affinity and information flow. DeltaCon is well-suited for comparing weighted, undirected networks and provides interpretable similarity scores that reflect both local and global structure.

The DeltaCon method provides a robust and reliable approach for analyzing biological networks. It leverages network affinities, meaning it evaluates how information flows through the whole network. One of its key strengths is that it accounts not only for the presence of edges, but also for their relative importance and the overall network topology, resulting in a more comprehensive and insightful analysis compared to simpler or less nuanced methods.

DeltaCon distances and similarities are sensitive to the size and connectivity of the networks being analyzed. To address this issue, we constructed weighted, tissue-aware, and balanced null distributions of DeltaCon scores that allow us to compute P-Values, indicating whether the observed similarity between networks is higher than expected by chance compared to a biologically relevant background. These null distributions are generated from real subnetworks of matched size and connectivity, sampled from each corresponding tissue graph, and excluding comparisons of networks with a high percentage of shared samples. This approach ensures that variations in network size and connectivity are properly accounted for, leading to a fair and biologically meaningful comparison. P-Value stability was evaluated across replicates with leave-one-out method for all networks in a tissue. Accordingly, 10 observed DeltaCon similarity values were obtained within 16 randomly selected size and connectivity combinations (n=160 observations). Overall, P-Values were stable across replicates showing low (\<0.1) coefficients of variation.

Read more about how the network similarity is visualized and interpreted under [DINA Network Similarity](docId\:L9yL9BcjG9h7jT0f8TX0f) and about our method validations under [DINA Network Similarity Validations](docId\:UhClpeE8-Fosx5LPlvkQT).

# References

1. D. Koutra, J. T. Vogelsteiny, C. Faloutsos, DELTACON: A Principled Massive-Graph Similarity Function. Proceedings of the 2013 SIAM International Conference on Data Mining, SDM 2013 , 1304–4657 (2013). ([https://dl.acm.org/doi/10.1145/2824443](https://dl.acm.org/doi/10.1145/2824443))