16.3 Graphs With Weighted Edges🔗

Consider a transportation graph: we are usually interested not only in whether we can get from one place to another, but also in what it “costs” (where we may have many different cost measures: money, distance, time, units of carbon dioxide, etc.). On the Internet, we might care about the ☛ latency or ☛ bandwidth over a link. Even in a social network, we might like to describe the degree of closeness of a friend. In short, in many graphs we are interested not only in the direction of an edge but also in some abstract numeric measure, which we call its weight.

In the rest of this study, we will assume that our graph edges have weights. This does not invalidate what we’ve studied so far: if a node is reachable in an unweighted graph, it remains reachable in a weighted one. But the operations we are going to study below only make sense in a weighted graph.We can, however, always treat an unweighted graph as a weighted one by giving every edge the same, constant, positive weight (say one).

Exercise

When treating an unweighted graph as a weighted one, why do we care that every edge be given a positive weight?

Exercise

Revise the graph data definitions to account for edge weights.

Exercise

Weights are not the only kind of data we might record about edges. For instance, if the nodes in a graph represent people, the edges might be labeled with their relationship (“mother”, “friend”, etc.). What other kinds of data can you imagine recording for edges?