# An interdisciplinary survey of network similarity methods

## TL;DR

This paper provides an accessible, interdisciplinary overview of network similarity methods across biology and pattern recognition, supported by a citation network analysis to highlight key objectives and methods.

## Contribution

It offers an integrative survey bridging graph comparison techniques in different fields, with quantitative analysis of influential papers via citation network metrics.

## Key findings

- Identification of key papers using centrality measures
- Analysis of community structure in citation network
- Comparison of objectives across disciplines

## Abstract

Comparative graph and network analysis play an important role in both systems biology and pattern recognition, but existing surveys on the topic have historically ignored or underserved one or the other of these fields. We present an integrative introduction to the key objectives and methods of graph and network comparison in each field, with the intent of remaining accessible to relative novices in order to mitigate the barrier to interdisciplinary idea crossover. To guide our investigation, and to quantitatively justify our assertions about what the key objectives and methods of each field are, we have constructed a citation network containing 5,793 vertices from the full reference lists of over two hundred relevant papers, which we collected by searching Google Scholar for ten different network comparison-related search terms. We investigate its basic statistics and community structure, and frame our presentation around the papers found to have high importance according to five different standard centrality measures.