influence-propagation

Influence propagation traces diffusion of ideas and behaviors across social networks by analyzing direct and indirect adoption paths. It maps adoption timelines, calculates reach multipliers, and identifies influential users using second-order network analysis and Laplacian diffusion to reveal how information flows through network nodes.

Yes. Second-order network analysis with condensed stacks and cellular sheaves reveals indirect influence paths across multiple connection degrees. This uncovers how ideas propagate through intermediary users and non-obvious network structures, enabling detection of hidden influence routes and secondary adoption waves.

Network diffusion analysis generates adoption_timeline, adoption_rate, and key_amplifiers datasets. These outputs quantify when ideas reach users, the speed of propagation, and which users drive the most influence, supporting adoption forecasting and influencer identification in social networks.

Influence propagation supports relational schemas and SQL queries for network_nodes, influence_edges, and idea_adoptions tables. This enables structured storage and retrieval of network topology, relationship strengths, and adoption records for downstream analysis and reporting.

Use network-level analysis to understand systemic propagation patterns, adoption cascades, and collective reach. Individual metrics suit tracking specific users. Influence propagation combines both: mapping perspective clusters and identifying key_amplifiers reveals where network structure amplifies or constrains information spread.