
Upload Video
videos in mp4/mov/flv
close
Upload video
Note: publisher must agree to add uploaded document 
Upload Slides
slides or other attachment
close
Upload Slides
Note: publisher must agree to add uploaded document 
Feedback
help us improve
close
Feedback
Please help us improve your experience by sending us a comment, question or concern
Please help transcribe this video using our simple transcription tool. You need to be logged in to do so.
Description
The resilience of networks to various types of failures is an undercurrent in many parts of graph theory and network algorithms. In this paper we study the resilience of networks in the presence of {\em cascading failures}  failures
that spread from one node to another across the network structure. One finds such cascading processes at work in the kind of contagious failures that spread among financial institutions during a financial crisis, through nodes of a power grid or communication network during a widespread outage, or through a human population during the outbreak of an epidemic disease.
A widely studied model of cascades in networks assumes that each node $v$ of the network has a threshold $\ell(v)$, and fails if it has at least $\ell(v)$ failed neighbors. We assume that each node selects a threshold $\ell(v)$ independently using a probability distribution $\mu$. Our work centers on a parameter that we call the $\mu$risk of a graph: the maximum failure probability of any node in the graph, in this threshold cascade model parameterized by threshold distribution $\mu$. This defines a very broad class of models; for example, the large literature on edge percolation, in which propagation happens along edges that are included independently at random with
some probability $p$, takes place in a small part of the parameter space of threshold cascade models, and one where the distribution $\mu$ is monotonically decreasing with the threshold. In contrast we want to study the whole space, including threshold distributions with qualitatively different behavior,
such as those that are sharply increasing.
We develop techniques for relating differences in $\mu$risk to the structures of the underlying graphs. This is challenging in large part because,
despite the simplicity of its formulation, the threshold cascade model has been very hard to analyze for arbitrary graphs $G$ and arbitrary threshold distributions $\mu$. It turns out that when selecting among a set of graphs to
minimize the $\mu$risk, the result depends quite intricately on $\mu$. We develop several techniques for evaluating the $\mu$risk of $d$regular graphs.
For $d=2$ we are able to solve the problem completely: the optimal graph is always a clique (i.e.\ triangle) or tree (i.e.\ infinite path), although which graph is better exhibits a surprising nonmonotonicity as the threshold parameters vary. When $d>2$ we present a technique based on powerseries expansions of the failure probability that allows us to compare graphs in certain parts of the parameter space, deriving conclusions including the fact that as $\mu$ varies, at least three different graphs are optimal among $3$regular graphs. In particular, the set of optimal 3regular graphs includes one which is neither a clique nor a tree.