#network infusion

Path based network diffusion kernel is its tenability, that it can consider different number of shortest paths in kernel computation. This resembles, vaguely, a Taylor expansion of network topology to form a diffusion kernel with different orders of expansion. One can extend this key idea to design other network diffusion kernels to approximate other general diffusion models such as SIR the challenge of determining sources is compounded as the true propagation dynamics are typically unknown, and when they have been directly measured, they rarely conform to the assumptions of any of the well studied models. In this paper introduce a method called Network Infusion NI that has been designed to circumvent these issues, making source inference practical for large, complex real world networks. A stateless receiver based multicast RBMulticast protocol that simply uses a list of the multicast members' e.g., sinks' addresses, embedded in packet headers, to enable receivers to decide the best way to forward the multicast traffic. This protocol, called Receiver Based Multicast, exploits the knowledge of the geographic locations of the nodes to remove the need for costly state maintenance. The key idea is that to infer the source node in the network, full characterization of diffusion dynamics, in many cases, may not be necessary. This objective is achieved by creating a diffusion kernel that well approximates standard diffusion models such as the susceptible infected diffusion model, but lends itself to inversion, by design, via likelihood maximization or error minimization. We apply NI for both single source and multi source diffusion, for both single snapshot and multi snapshot observations, and for both homogeneous and heterogeneous diffusion setups. We prove the mean field optimality of NI for different scenarios, and demonstrate its effectiveness over several synthetic networks. Moreover, we apply NI to a real data application, identifying news sources in the Digg social network, and demonstrate the effectiveness of NI compared to existing methods.