New products, ideas, norms and behaviors are often thought to propagate through a person-to-person diffusion process analogous to the spread of an infectious disease. Until recently, however, it has been prohibitively difficult to directly observe this process, and thus to rigorously quantify or characterize the structure of information cascades. In one of the largest studies to date, we describe the diffusion structure of billions of events across several domains. We find that the vast majority of cascades are small, and are characterized by a handful of simple tree structures that terminate within one degree of an initial adopting "seed." While large cascades are extremely rare, the scale of our data allows us to investigate even the one-in-a-million events. To study these rare, large cascades, we develop a formal measure of what we label "structural virality" that interpolates between two extremes: content that gains its popularity through a single, large broadcast, and that which grows via a multi-generational cascade where any one individual is directly responsible for only a fraction of the total adoption. We find that the very largest observed events nearly always exhibit high structural virality, providing some of the first direct evidence that many of the most popular products and ideas grow through person-to-person diffusion. However, medium-sized events -- having thousands of adopters -- exhibit surprising structural diversity, and are seen to grow both through broadcast and viral means. Finally, we show that our empirical results are largely consistent with an SIR model of contagion on a scale-free network, reminiscent of previous work on the long-term persistence of computer viruses.
- Un nouvel algorithme de clustering pour la détection d’anomaliesDamien Nogues2017, January 26, Room 24-25/405
- affinity index algorithm analysis antipaedo attack bipartite blog network blogs capitalisme social Cascade centrality clustering communities community detection community structure complex network complex networks complex systems compression connected graphs data mining debian degree distribution degree peeling diameter diffusion diffusion phenomena distributed measurements DynamicNetworks dynamics edge-Markovian evolving graph eDonkey ego-centered ego-centered communities email epidemiology event detection evolving graphs evolving networks exploration failure fixed points formal concepts gossip graph graph algorithm graph decompositions Graphs hierarchical clustering honeypot influence influence ranking interaction networks internal links internet Internet topology intrinsic time IP-level ip exchanges lattice leaders link prediction long term communities markovian model measurement mesure d’influence metrics Metrology mobile networks Modelling modularity multi-ego-centered communities multi-scale multipartite graph network dynamics node proximity node similarity opinion dynamics outliers p2p P2P dynamics P2P networks parametric paris paris-traceroute path-vector routing pedophile activity phone power-law radar random graph random walks reachability robustness routing routing tables scale-free security simulation simulations sir social networks spreading spreading cascades stability statistical analysis stochastic process three-state cellular automata time-varying Topology traceroute triangles twitter UDP user profiles viral marketing visualization web wifi