When Euler invented the first graph, he was trying to solve a very specific problem of the citizens of Königsberg, with a very specific representation/model and a very specific algorithm. It turns out that there are quite a few problems that can be:
Described using the graph metaphor of objects and pairwise relations between these objects
Solved by applying a mathematical algorithm to this structure
The mechanism is the same, and the scientific discipline that studies these modeling and solution patterns, using graphs, is often referred to as the graph theory, and it is considered to be a part of discrete Mathematics.
There are lots of different types of graphs that have been analyzed in this discipline, as you can see from the following diagram.
Graph theory, the study of graph models and algorithms, has turned out to be a fascinating field of study, which has been used in many different disciplines to solve some of the most interesting questions facing mankind. Interestingly enough, it has seldom really been applied with rigor in the different fields of science that can benefit from it; maybe scientists today don't have the multidisciplinary approach required (providing expertise from graph theory and their specific field of study) to do so.
So, let's talk about some of these fields of study a bit, without wanting to give you an exhaustive list of all applicable fields. Still, I do believe that some of these examples will be of interest for our future discussions in this book and work up an appetite for what types of applications we will use a graph-based database such as Neo4j for.
For the longest time, people have understood that the way humans interact with one another is actually very easy to describe in a network. People interact with people every day. People influence one another every day. People exchange ideas every day. As they do, these interactions cause ripple effects through the social environment that they inhabit. Modeling these interactions as a graph has been of primary importance to better understand global demographics, political movements, and—last but not least—commercial adoption of certain products by certain groups. With the advent of online social networks, this graph-based approach to social understanding has taken a whole new direction. Companies such as Google, Facebook, Twitter, LinkedIn (see the following diagram featuring a visualization of my LinkedIn network), and many others have undertaken very specific efforts to include graph-based systems in the way they target their customers and users, and in doing so, they have changed many of our daily lives quite fundamentally.
We sometimes say it in marketing taglines: "Graphs Are Everywhere". When we do so, we are actually describing reality in a very real and fascinating way. Also, in this field, researchers have known for quite some time that biological components (proteins, molecules, genes, and so on) and their interactions can accurately be modeled and described by means of a graph structure, and doing so yields many practical advantages. In metabolic pathways (see the following diagram for the human metabolic system), for example, graphs can help us to understand how the different parts of the human body interact with each other. In metaproteomics, researchers analyze how different kinds of proteins interact with one another and are used in order to better steer chemical and biological production processes.
A diagram representing the human metabolic system
Some of the earliest computers were built with graphs in mind. Graph Compute Engines solved scheduling problems for railroads as early as the late 19th century, and the usage of graphs in computer science has only accelerated since then. In today's applications, the use cases vary from chip design, network management, recommendation systems, and UML modeling to algorithm generation and dependency analysis. The following is an example of such a UML diagram:
An example of a UML diagram
The latter is probably one of the more interesting use cases. Using pathfinding algorithms, software and hardware engineers have been analyzing the effects of changes in the design of their artifacts on the rest of the system. If a change is made to one part of the code, for example, a particular object is renamed; the dependency analysis algorithms can easily walk the graph of the system to find out what other classes will be affected by the former change.
Another really interesting field of graph theory applications is flow problems, also known as maximum flow problems. In essence, this field is part of a larger field of optimization problems, which is trying to establish the best possible path across a flow network. Flow networks are a type of graph in which the nodes/vertices of the graph are connected by relationships/edges that specify the capacity of that particular relationship. Examples can be found in fields such as telecom networks, gas networks, airline networks, package delivery networks, and many others, where graph-based models are then used in combination with complex algorithms. The following diagram is an example of such a network, as you can find it on http://enipedia.tudelft.nl/.
An example of a flow network
These algorithms are then used to identify the calculated optimal path, find bottlenecks, plan maintenance activities, conduct long-term capacity planning, and many other operations.
The original problem that Euler set out to solve in 18th century Königsberg was in fact a route planning / pathfinding problem. Today, many graph applications leverage the extraordinary capability of graphs and graph algorithms to calculate—as opposed to finding with trial and error—the optimal route between two nodes on a network. In the following diagram, you will find a simple route planning example as a graph:
A simple route planning example between cities to choose roads versus highways
A very simple example will be from the domain of logistics. When trying to plan for the best way to get a package from one city to another, one will need the following:
A list of all routes available between the cities
The most optimal of the available routes, which depends on various parameters in the network, such as capacity, distance, cost, CO2 exhaust, speed, and so on
This type of operation is a very nice use case for graph algorithms. There are a couple of very well-known algorithms that we can briefly highlight:
The Dijkstra algorithm: This is one of the best-known algorithms to calculate the shortest weighted path between two points in a graph, using the properties of the edges as weights or costs of that link.
The A* (A-star) algorithm: This is a variation of Dijkstra's original ideas, but it uses heuristics to predict more efficiently the shortest path explorations. As A* explores potential graph paths, it holds a sorted priority queue of alternate path segments along the way, since it calculates the "past path" cost and the "future path" cost of the different options that are possible during the route exploration.
Depending on the required result, the specific dataset, and the speed requirements, different algorithms will yield different returns.
No book chapter treating graphs and graph theory—even at the highest level—will be complete without mentioning one of the most powerful and widely-used graph algorithms on the planet, PageRank. PageRank is the original graph algorithm, invented by Google founder Larry Page in 1996 at Stanford University, to provide better web search results. For those of us old enough to remember the early days of web searching (using tools such as Lycos, AltaVista, and so on), it provided a true revolution in the way the Web was made accessible to end users. The following diagram represents the PageRank graph:
The older tools did keyword matching on web pages, but Google revolutionized this by no longer focusing on keywords alone, but by doing link analysis on the hyperlinks between different web pages. PageRank, and many of the other algorithms that Google uses today, assumes that more important web pages, which should appear higher in your search results, will have more incoming links from other pages, and therefore, it is able to score these pages by analyzing the graph of links to the web page. History has shown us the importance of PageRank. Not only has Google, Inc. built quite an empire on top of this graph algorithm, but its principles have also been applied to other fields such as cancer research and chemical reactions.