In 1970, mathematician Jeff Cheeger proved that Laplacians are good at detecting bottlenecks in surfaces and higher dimensional objects called manifolds. The reason is especially apparent in the case of heat flow. If a metal bar is shaped like an hourglass, the constriction means that the first eigenvalue of the Laplacian will be small. In turn, this implies that the relaxation time will be longer than for a rectangular bar, and that is indeed what one measures.
The Laplacian on a network is defined analogously to the Laplacian on a surface. It is again computed by taking the difference between a function’s values at a point and the average of its values at the neighboring points. In 1985, it was shown that Cheeger’s result applied perfectly to this new setting. The first eigenvalue of the Laplacian, which is relatively easy to compute, is a good proxy for the width of the narrowest bottleneck, which is harder to determine.
A network has a bottleneck if it contains two (large) subgraphs with (relatively) few connections between them. (There are some subtleties connected with the words “large” and “relatively.”) A driveway isn’t a bottleneck, because if you cut it you take only one house out of the network. However, if two large cities are joined by only one bridge—such as the Ambassador Bridge between Detroit and Windsor, Canada—that bridge is definitely a bottleneck. Even if you include the Detroit-Windsor Tunnel, creating a second link between the cities, the ratio of the number of cuts required to disconnect the network (two) to the minimum number of people taken out of the network (216,000 on the Windsor side) is still very small (2/236,000, or 0.00001) (see Figure 18).
18 / The Ambassador Bridge connecting Detroit, Michigan, and Windsor, Ontario, is a very visible example of a “miminum cut.” /