BackgroundPrimary care networks (PCNs) are a new organisational hierarchy introduced in the NHS Long Term Plan with wide-ranging responsibilities. The vision is that they represent ‘natural’ communities of general practices with boundaries that make sense to practices, other healthcare providers, and local communities.AimOur study aims to identify natural communities of general practices based on patient registration patterns, using network analysis methods and unsupervised clustering to create catchments for these communities.MethodPatients resident in and attending GP practices in London were identified from Hospital Episode Statistics from 2017 to 2018. We used a series of novel methods for unsupervised graph clustering. A cosine similarity matrix was constructed representing similarities between each general practice to each other, based on registration of patients in each Lower Super Output Area (LSOA). Unsupervised graph partitioning using Markov Multiscale Community Detection was conducted to identify communities of general practices. Catchments were assigned to each PCN based on the majority attendance from an LSOA.ResultsIn total 3 428 322 unique patients attended 1334 GPs in general practices LSOAs in London. The model grouped 1291 general practices (96.8%) and 4721 LSOAs (97.6%), into 165 mutually exclusive PCNs. The median PCN list size was 53 490 and a median of 70.1% of patients attended a general practice within their allocated PCN, ranging from 44.6% to 91.4%.ConclusionWith PCNs expected to take a role in population health management and with community providers expected to reconfigure around them, it is vital we recognise how PCNs represent their communities. This method may be used by policymakers to understand the populations and geography shared between networks.