Winner takes all: competing viruses or ideas on fair-play networks
Proceedings of the 21st international conference on World Wide Web
Propagation and immunization in large networks
XRDS: Crossroads, The ACM Magazine for Students - Big Data
Rise and fall patterns of information diffusion: model and implications
Proceedings of the 18th ACM SIGKDD international conference on Knowledge discovery and data mining
Interacting viruses in networks: can both survive?
Proceedings of the 18th ACM SIGKDD international conference on Knowledge discovery and data mining
Understanding and managing cascades on large graphs
Proceedings of the VLDB Endowment
Competing memes propagation on networks: a case study of composite networks
ACM SIGCOMM Computer Communication Review
Gelling, and melting, large graphs by edge manipulation
Proceedings of the 21st ACM international conference on Information and knowledge management
Phase transition of multi-state diffusion process in networks
ACM SIGMETRICS Performance Evaluation Review
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Given a network of who-contacts-whom or who links-to-whom, will a contagious virus/product/meme spread and 'take-over' (cause an epidemic) or die-out quickly? What will change if nodes have partial, temporary or permanent immunity? The epidemic threshold is the minimum level of virulence to prevent a viral contagion from dying out quickly and determining it is a fundamental question in epidemiology and related areas. Most earlier work focuses either on special types of graphs or on specific epidemiological/cascade models. We are the first to show the G2-threshold (twice generalized) theorem, which nicely de-couples the effect of the topology and the virus model. Our result unifies and includes as special case older results and shows that the threshold depends on the first eigenvalue of the connectivity matrix, (a) for any graph and (b) for all propagation models in standard literature (more than 25, including H.I.V.) [20], [12]. Our discovery has broad implications for the vulnerability of real, complex networks, and numerous applications, including viral marketing, blog dynamics, influence propagation, easy answers to 'what-if' questions, and simplified design and evaluation of immunization policies. We also demonstrate our result using extensive simulations on one of the biggest available social contact graphs containing more than 31 million interactions among more than 1 million people representing the city of Portland, Oregon, USA.