graph-analysis

To analyze follower networks, you can build a network graph from follower relationships and run algorithms like pagerank and betweenness to identify clusters, influencers, and bridge accounts. This reveals audience segments and cross-audience reach.

Detecting communities in a network graph involves running community detection algorithms to map audience structures. Bridge accounts are identified as nodes connecting separate clusters, enabling you to expand cross-audience reach.

Yes, you can export graph data and visualize networks directly for reporting. The analysis supports visualization options to map follower networks and reveal clusters, making audience structures clear for growth teams.

Graph analysis works for mapping audience segments by building network graphs from follower relationships. It identifies clusters and bridge accounts, allowing growth teams and brand monitors to assess cross-audience reach effectively.

The algorithms used to find top influencers in a follower network include pagerank, betweenness, and community algorithms. These calculate node influence and connectivity to pinpoint key accounts within audience segments.

Using betweenness and pagerank when analyzing follower networks highlights bridge accounts and top influencers. Betweenness reveals cross-audience reach by finding connecting nodes, while pagerank ranks overall influence within clusters.