# 5.13. Glossary¶

**Breadth-First Search:** This algorithm starts at the root of the tree and explores all of the neighbor nodes at the present level before moving on to the nodes at the next level.

**Clique:** A set of nodes that are completely connected; that is, there are edges between all pairs of nodes in the set.

**Clustering Coefficient:** A measure of the degree to which nodes in a graph tend to cluster together.

**Clustering:** A measure of the “cliquishness” of the graph.

**Depth-First Search:** An algorithm for traversing or searching a tree or graph data structures. It starts at the root node and explores as far as possible along each branch before backtracking.

**Degree:** The number of neighbors a node has.

**Dijkstra’s Algorithm:** Solves the “single source shortest path problem”, which means that it finds the minimum distance from a given “source” node to every other node in the graph (or at least every connected node).

**Generative Model:** Tries to explain a phenomenon by modeling the process that builds or leads to the phenomenon.

**Path Length:** A measure of the average distance between two nodes.

**Queue:** A data structure in which elements are removed in the same order they were entered.

**Regular Graphs:** In a regular graph every node has the same number of neighbors.

**Ring Lattice:** Is a kind of regular graph, with \(n\) nodes, the nodes can be arranged in a circle with each node connected to the \(k\) nearest neighbors.

**Watts-Strogatz Graphs:** A random graph generation model that produces graphs with small-world properties, including short average path lengths and high clustering.