Locating the Source of Diffusion in Large-Scale Networks
This addresses the challenge of source localization in large networks where full observation is infeasible, offering practical solutions for domains like epidemiology or cybersecurity, though it appears incremental as it builds on existing diffusion models.
The paper tackles the problem of localizing the source of diffusion in large-scale networks, such as the Internet or social graphs, by showing it is possible to estimate the source location using sparse observer measurements, with an optimal strategy for arbitrary trees achieving maximum probability of correct localization and efficient implementations.
How can we localize the source of diffusion in a complex network? Due to the tremendous size of many real networks--such as the Internet or the human social graph--it is usually infeasible to observe the state of all nodes in a network. We show that it is fundamentally possible to estimate the location of the source from measurements collected by sparsely-placed observers. We present a strategy that is optimal for arbitrary trees, achieving maximum probability of correct localization. We describe efficient implementations with complexity O(N^α), where α=1 for arbitrary trees, and α=3 for arbitrary graphs. In the context of several case studies, we determine how localization accuracy is affected by various system parameters, including the structure of the network, the density of observers, and the number of observed cascades.