Biased random walkers and extreme events on the edges of complex networks

Extreme events have low occurrence probabilities and display pronounced deviation from their average behavior, such as earthquakes or power blackouts. Such extreme events occurring on the nodes of a complex network have been extensively studied earlier through the modeling framework of unbiased random walks. They reveal that the occurrence probability for extreme events on nodes of a network has a strong dependence on the nodal properties. Apart from these, a recent work has shown the independence of extreme events on edges from those occurring on nodes. Hence, in this work, we propose a more general formalism to study the properties of extreme events arising from biased random walkers on the edges of a network. This formalism is applied to biases based on a variety network centrality measures including PageRank. It is shown that with biased random walkers as the dynamics on the network, extreme event probabilities depend on the local properties of the edges. The probabilities are highly variable for some edges of the network, while they are approximately a constant for some other edges on the same network. This feature is robust with respect to different biases applied to the random walk algorithm. Further, using the results from this formalism, it is shown that a network is far more robust to extreme events occurring on edges when compared to those occurring on the nodes.