1. Embedding network measurement and design in interventions

For example, in the paper “Network methods to support user involvement in qualitative data analyses: An introduction to Participatory Theme Elicitation” we applied network mapping within a participatory research project to enable lay researchers to see and engage with relational structures within qualitative data. The method illustrates how network thinking can inform even qualitative/participatory work: by visualising ties and patterns, by structuring involvement, by making relational structure explicit. This highlights that social network methods are not only about mapping friendships or influence but they can structure how data is interpreted, how participants are engaged, and how themes are generated.

2. Considering networks in behaviour change and public health trials

In our work on behaviour change interventions among adolescents, we used influence-agent selection based on network centrality (closeness) derived from friendship networks, and then trained these agents via smartphone to promote physical activity. Although the intervention did not produce the expected effect on overall physical activity, this study advanced the field in three ways:

• it used a greedy search algorithm to identify influence agents by network centrality rather than simpler heuristics;
• it demonstrated feasibility of smartphone-based training of network agents;

it used multilevel modelling to explicitly account for nested network/individual structure.

These methodological advances show how network methods can (and should) be integrated into behaviour-change research, even if outcome results are mixed.

3. Critically reflecting on network methods and motivation in public health

In the article “Physical activity and behaviour change: the role of distributed motivation” we reframed motivation as a property not just of the individual, but of systems that include social interaction and networked relations. This paper illustrates how network-aware thinking can shift conceptual foundations: rather than asking “how do we motivate the person?”, we ask “what network of ties, organisational structures, technologies, social interactions enable or disable motivation?” From a methods point of view, this work demonstrates how social network methods can deepen theoretical insight.

4. Advances in network method algorithms and generative models

Our methodological foundation also draws on work in network science and algorithms. For example, the generative-model paper in Algorithms (2015) presented a model for random graphs with labelled vertices and weighted edges, enabling more realistic simulation of social-network data.

This paper underpins our ability to apply network methods in complex health behaviour contexts when network data has weights, labels, multiplex ties, or other complexities. By incorporating network analysis tools (centrality, community detection, generative models) we are better placed to design, evaluate, and interpret health intervention studies that operate through network channels.

Implications for practice and future work

There are a number of practical and methodological implications from this work:

1. Measure networks early: Collect data on social ties, structural positions, central actors, community boundaries, network density, bridging ties. This enables you to embed network metrics in the design.
2. Select and train network actors thoughtfully: Rather than just choosing high-degree nodes, use structural metrics (e.g., closeness, betweenness, bridging ties) and algorithmic selection where feasible (as our adolescent study did).
3. Account for nested and network-driven variance: Use multilevel and network-aware statistical methods to account for dependencies between units (for example, friends of friends, clusters of influence).
4. Conceptually rethink behaviour change frameworks: Move from individual-only models to “relational” or “networked” models: how do peers, networks, brokers, sub‐communities support or hinder change?
5. Integrate advanced network science methods: Use generative models or simulations to test how network structure might moderate intervention effects; detect communities, weighted ties, multiplex layers, and so on.
6. Recognise limitations and context specificity: Network methods add complexity, data collection is challenging, dependency assumptions must be addressed, and network structures vary dynamically across contexts (schools vs communities vs online).