# Data Science at FMP: Network Science

DevFeed: [Data Science at FMP: Network Science](<https://devfeed.tech/articles/data-science-at-fmp-network-science-19742.md>)

Original publisher: [Read original article](<https://tech.findmypast.com/datsci-network-science/>)

Author: Anup Purewal

Published: 2021-08-23T00:00:00Z

Content type: article

Language: en

Sources: [Findmypast](<https://devfeed.tech/sources/findmypast.md>)

Topics: [Network](<https://devfeed.tech/topics/network.md>), [Graphs](<https://devfeed.tech/topics/graphs.md>), [Data Science](<https://devfeed.tech/topics/data-science.md>), [Statistics](<https://devfeed.tech/topics/statistics.md>), [Mathematics](<https://devfeed.tech/topics/mathematics.md>), [Machine learning](<https://devfeed.tech/topics/machine-learning.md>)

Tags: [data-science](<https://devfeed.tech/tags/data-science.md>), [graph](<https://devfeed.tech/tags/graph.md>), [machine-learning](<https://devfeed.tech/tags/machine-learning.md>), [network](<https://devfeed.tech/tags/network.md>), [product](<https://devfeed.tech/tags/product.md>), [statistics](<https://devfeed.tech/tags/statistics.md>)

## AI overview

Findmypast introduces its data science team and the first in a series of posts about its work. This article explains network science through nodes, edges, paths, directionality and measures, with examples illustrating how network analysis can represent real-world relationships and support genealogical research.

## Source excerpt

Data Science at FMP Findmypast is at the forefront of facilitating historical, and in particular, genealogical research. This has been achieved by embracing advances in technology that have allowed us to digitise vast quantities of records and make these easily available online. The result is a diverse user-base that covers everyone from advanced professional researchers through to those just starting out on their family history journey. Recently, we have taken another stride forward by establishing a data science team who utilise advanced mathematics, statistics and machine learning to improve a user's research experience. Can we help users who have a hit a brick wall in their research? Can we suggest the next record set that they should explore or the next search they should conduct? Can we help users glean information more easily from less structured texts such as newspapers? This is the first in a series of blogs that will give an insight into what the Data Science team is working on and more importantly how that helps our users. Network Science In simple terms, a network (or graph) is made up of nodes (vertices) connected in some fashion by a set of edges. The nodes can represent a variety of different real-world entities depending on the subject matter, for example people, places, animals, or neurons. The edges that link these nodes can be customised to represent different types of connectivity by introducing weighting or directionality. A path (or walk) through a network is a sequence of nodes and edges generated by traversing the network where the sequenced nodes and edges are incident to one another. In the very simple predator-prey (food chain) example below, the animals (and plant) are the nodes and the linking lines between them are the edges. The arrows on the edges indicate directionality, which in this example indicates who is the predator and who is the prey. For example, the rabbit consumes the plant as the arrow extends from the plant to the rabbit