The Graph Traversal Pattern

TL;DR

This paper discusses the graph traversal pattern, its historical context, and its significance in modern graph databases and computing, highlighting its role in processing interconnected data efficiently.

Contribution

It provides an overview of the graph traversal pattern, emphasizing its importance in the evolution of graph databases and data processing techniques.

Findings

Graph traversal is fundamental in modern graph databases.

Graph databases enable efficient local traversals without indexes.

The pattern has evolved alongside growing interconnected data.

Abstract

A graph is a structure composed of a set of vertices (i.e.nodes, dots) connected to one another by a set of edges (i.e.links, lines). The concept of a graph has been around since the late 19$^\text{th}$ century, however, only in recent decades has there been a strong resurgence in both theoretical and applied graph research in mathematics, physics, and computer science. In applied computing, since the late 1960s, the interlinked table structure of the relational database has been the predominant information storage and retrieval model. With the growth of graph/network-based data and the need to efficiently process such data, new data management systems have been developed. In contrast to the index-intensive, set-theoretic operations of relational databases, graph databases make use of index-free, local traversals. This article discusses the graph traversal pattern and its use in computing.