We leverage micro-creators and content aggregators with high betweenness centrality to bridge content across clustered communities. Our approach increases algorithmic cascading globally, especially when coupled with macro activations.
Every seeding decision and activation traces back to peer-reviewed science. From the strength of weak ties to the localization of global music.
Weak ties — acquaintances, loose connections — are the bridges that carry novel information between social clusters; strong-tie groups mostly recirculate what they already know.
Read more →A 20-million-person, 5-year LinkedIn experiment proved weak ties causally drive opportunity — with an inverted-U: moderately weak ties win, the very strongest and very weakest do not.
Read more →Across many simulations, large cascades are driven not by rare 'influentials' but by a critical mass of easily-influenced people; whether the network can cascade matters more than who starts it.
Read more →Behaviours that need social reinforcement from multiple sources (a 'complex contagion') spread on the WIDTH of bridges, not their length — long thin ties can even block them.
Read more →In a controlled experiment a behaviour spread to more people and ~4x faster through clustered, redundant networks than through random 'small-world' ones; a second reinforcing signal sharply lifted adoption.
Read more →Across field experiments and a 200,000-customer campaign, the best seeding strategy was up to 8x more effective than the worst; seeding hubs and bridges beat random and fringe — because they participate more and reach further.
Read more →Formalises 'pick the k seeds that maximise spread'; introduces the Independent Cascade and Linear Threshold models and a greedy algorithm with a provable ~63% optimality guarantee.
Read more →A 253-million-user Facebook experiment: feed exposure made people 7.4x likelier to share; strong ties are individually more influential, but the far more numerous weak ties carry most of the spread and the novel information.
Read more →A 1.3-million-user music experiment: listening 'caught on' to a fan's friends only for already-popular artists, and the effect grew with the number of friends involved — emerging artists got no organic spillover.
Read more →Causal estimates of playlist placement: a slot on 'Today's Top Hits' was worth roughly 19.4 million streams on average — placement, not just quality, drives outcomes.
Read more →Frames influencers as high-centrality hubs, algorithms as 'super-edges', and feed exposure as the transmission mechanism, recommending a hybrid 'test on TikTok, refine on Instagram' approach.
Read more →A network analysis of Spotify's related-artist graph found international-label acts get cross-border, genre-based recommendations while local-label and independent acts are paired by country of origin. (Case study; treat as a structural tendency.)
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