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.