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# What is graph theory?

by Stephen M. Walker II, Co-Founder / CEO

## What is graph theory?

Graph theory is a branch of mathematics that studies graphs, which are mathematical structures used to model pairwise relations between objects. In this context, a graph is made up of vertices (also known as nodes or points) which are connected by edges. The vertices represent objects, and the edges represent the relationships between these objects.

Graph theory can be applied to both directed and undirected graphs. In a directed graph, the edges link two vertices asymmetrically, meaning there is a direction to the relationship. In contrast, in an undirected graph, the edges link two vertices symmetrically, meaning the relationship is bidirectional.

Graph theory has a wide range of applications across various fields. For instance, in social sciences, it's used to measure actors' prestige or to explore rumor spreading through the use of social network analysis software. In computer science, it's used to solve problems related to network structures and connections. In discrete mathematics, it's used to study the relationships between vertices and edges.

The history of graph theory can be traced back to 1735 when Swiss mathematician Leonhard Euler solved the Königsberg bridge problem, which involved finding a path over every bridge without crossing any bridge twice.

Graph theory also includes the study of special types of graphs. For example, a graph is called Eulerian if it contains an Eulerian circuit, which is a closed trail that includes every edge once. Another concept in graph theory is graph coloring, where each vertex in a graph is assigned a color such that no two adjacent vertices share the same color.

In addition to these, graph theory also deals with weighted graphs, where each edge is assigned a numerical weight. This is particularly useful in scenarios where the pairwise connections have some numerical values, such as in a graph representing a road network where the weights could represent the length of each road.

## What is a graph?

A graph can be understood in two primary contexts: in general usage, it refers to a diagram that represents the variation of a variable in comparison with that of one or more others. This includes line graphs, bar graphs, pie charts, scatter plots, histograms, and more. These types of graphs are commonly used in various fields such as statistics, data science, economics, and business to visually represent data and display statistics.

In the context of mathematics, particularly in discrete mathematics and graph theory, a graph is a structure made up of vertices (also called nodes or points) and edges that join pairs of vertices. This structure is used to model pairwise relations between objects. The edges may be directed (from one vertex to another) or undirected (bi-directional). Graph theory has numerous applications in fields like computer science, electrical engineering, linguistics, physics, chemistry, social sciences, biology, and mathematics.

In computer science, for instance, graph theory is used for the study of algorithms. In social networks, graph theory can represent complex networks of users in a compact way, clustering users based on different similarities. In the world of data science, one popular application of graph theory is in solving flow problems.

So, depending on the context, a graph can either be a visual representation of data or a mathematical structure used to model relationships between pairs of objects.

## More terms

### What are rule-based systems in AI?

Rule-based systems in AI are a type of artificial intelligence system that relies on a set of predefined rules or conditions to make decisions or take actions. They use an "if-then" logic structure, where certain inputs trigger specific outputs based on the defined rules. They are commonly used in applications such as expert systems, decision support systems, and process control systems.

### What is Statistical Representation Learning?

Statistical Representation Learning (SRL) is a set of techniques in machine learning and statistics that allows a system to automatically discover the representations needed for feature detection or classification from raw data. This process replaces manual feature engineering and allows a machine to both learn the features and use them for tasks such as classification.