COMPUTING SCIENCE

# Graph Theory in Practice: Part I

# Euler to Erdos

Graph theory got its start in the 18th century, when the great Swiss-born mathematician Leonhard Euler solved the puzzle of the Königsberg bridges. At the time, Königsberg (now Kaliningrad) had seven bridges spanning branches of the Pregel River. The puzzle asked whether a walk through the city could cross each bridge exactly once. The problem can be encoded in a graph (actually a multigraph) by representing the land areas as vertices and the bridges as edges:

Euler showed that you can answer the question by tabulating the degree*,* or valency*,* of each vertex—the number of edges meeting there. If a graph has no more than two odd vertices, then some path traverses each edge once. In the Königsberg graph all four vertices are odd.

The techniques of graph theory soon proved useful for more than planning a stroll along the Pregel. The German physicist Gustav Kirchoff analyzed electric circuits in terms of graphs, with wires as edges and junction points as vertices. Chemists found a natural correspondence between graphs and the structural diagrams of molecules: An atom is a vertex, and an edge is a bond between atoms. Graphs also describe communications and transportation networks, and even the neural networks of the brain. Other applications are less obvious. For example, a chess tournament is a graph: The players are nodes, and matches are edges. An economy is also a graph: Companies or industries are nodes, and edges represent transactions.

In the 20th century graph theory has become more statistical and algorithmic. One rich source of ideas has been the study of random graphs, which are typically formed by starting with isolated vertices and adding edges one at a time. The master of this field was the late Paul Erdos. With his colleague Alfred Rényi, Erdos made the central finding that a "giant component"—a connected piece of the graph spanning most of the vertices—emerges suddenly when the number of edges exceeds half the number of vertices.

The recent work on the World Wide Web and other very large graphs is also statistical and algorithmic in nature, and it has close ties to the Erdos-Rényi theory of random graphs. But there is a new twist. Many of these huge graphs are not deliberate constructions but natural artifacts that have grown through some accretionary or evolutionary process. The Web, in particular, is an object no one designed. On close examination, the structure of such graphs seems neither entirely random nor entirely regular. Understanding the balance of order and chaos in these graphs is one of the aims of the current undertakings. A more basic goal is simply finding computational techniques that will not choke on a graph with 10^{8} nodes.

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**Of Possible Interest**

**Computing Science**: Belles lettres Meets Big Data

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