Herein lies Part the Second of *n* posts introducing various network concepts. Part the First can be found here. From here on out, the posts will be cover only one topic at a time. I’ll occasionally use math, but will do my best to explain it from the ground up, assuming no previous knowledge of mathematical notation.

# Node Degree: An Introduction

Today I’ll cover the deceptively simple concept of *node degree*. I say “deceptive” because, on the one hand, network degree can tell you quite a lot. On the other hand, degree can often lead one astray, especially as networks become larger and more complicated.

A node’s *degree** *is, simply, how many edges it is connected to. Generally, this also correlates to how many *neighbors* a node has, where a node’s neighborhood is those other nodes connected directly to it by an edge. In the network below, each node is labeled by its degree.

If you take a minute to study the network, something might strike you as odd. The bottom-right node, with degree 5, is connected to only four distinct edges, and really only three other nodes (four, including itself). *Self-loops*, which will be discussed later because they’re annoying and we hates them preciousss, are counted twice. A self-loop is any edge which starts and ends at the same node.

*Why* are self-loops counted twice? Well, as a rule of thumb you can say that, since the degree is the number of times the node is connected to an edge, and a self-loop connected to a node twice, that’s the reason. There are some more mathy reasons dealing with matrix representation, another topic for a later date. Suffice to say that many network algorithms will not work well if self-loops are only counted once.

The odd node out on the bottom left, with degree zero, is called an *isolate*. An isolate is any node with no edges.

At any rate, the concept is clearly simple enough. Count the number of times a node is connected to an edge, get the degree. If only getting higher education degrees were this easy.

# Centrality

Node degree is occasionally called *degree centrality*. Centrality is generally used to determine how important nodes are in a network, and lots of clever researchers have come up with lots of clever ways to measure it. “Importance” can mean a lot of things. In social networks, centrality can be the amount of influence or power someone has; in the U.S. electrical grid network, centrality might mean which power station should be removed to cause the most damage to the network.

The simplest way of measuring node importance is to just look at its degree. This centrality measurement at once seems deeply intuitive and extremely silly. If we’re looking at the social network of facebook, with every person a node connected by an edge to their friends, it’s no surprise that the most well-connected person is probably also the most powerful and influential in the social space. On the same token, though, degree centrality is such a coarse-grained measurement that it’s really anybody’s guess what *exactly* it’s measuring. It could mean someone has a lot of power; it could also mean that someone tried to become friends with absolutely everybody on facebook.

## Degree Centrality Sampling Warnings

Degree works best as a measure of network centrality when you have *full knowledge* of the network. That is, a social network exists, and instead of getting some glimpse of it and analyzing just that, you have the entire context of the social network: all the friends, all the friends of friends, and so forth.

When you have an *ego-network* (a network of one person, like a list of all my friends and who among them are friends with one another), clearly the node with the highest centrality is the ego node itself. This knowledge tells you very little about whether that ego is actually central within the larger network, because you sampled the network *such that the ego is necessarily the most central*. Sampling strategies – how you pick which nodes and edges to collect – can fundamentally affect centrality scores.

A historian of science might generate a correspondence network from early modern letters currently held in Oxford’s library. In fact, this is currently happening, and the resulting resource will be invaluable. Unfortunately, centrality scores generated from nodes in that early modern letter writing network will more accurately reflect the whims of Oxford editors and collectors over the years, rather than the underlying correspondence network itself. Oxford scholars over the years selected certain collections of letters, be they from Great People or sent to or from Oxford, and that choice of what to hold at Oxford libraries will bias centrality scores toward Oxford-based scholars, Great People, and whatever else was selected for.

Similarly, the generation of a social network from a literary work will bias the recurring characters; characters that occur more frequently are simply statistically more likely to appear with more people, and as such will have the highest degrees. It is likely that the degree centrality and frequency of character occurrence are almost exactly correlated.

Of course, if what you’re looking for is *the most central character in the novel* or *the most central figure from Oxford’s perspective*, this measurement might be perfectly sufficient. The important thing is to be aware of the limitations of degree centrality, and the possible biasing effects from selection and sampling. Once those biases are explicit, careful and useful inferences can still be drawn.

Things get a bit more complicated when looking at document similarity networks. If you’ve got a network of books with edges connecting them based on whether they share similar topics or keywords, your degree centrality score will mean something *very different*. In this case, centrality could mean the most general book. Keep in mind that book length might affect these measurements as well; the longer a book is, the more likely (by chance alone) it will cover more topics. Thus, longer books may also appear to be more central, if one is not careful in generating the network.

## Degree Centrality in Bi-Modal Networks

Recall that bi-modal networks are ones where there are two different types of nodes (e.g., articles and authors), and edges are relationships that bridge those types (e.g., authorships). In this example, the more articles an author has published, the more central she is. Degree centrality would have nothing to do, in this case, with the number of co-authorships, the position in the social network, etc.

With an even more multi-modal network, having many types of nodes, degree centrality becomes even less well defined. As the sorts of things a node can connect to increases, the utility of simply counting the number of connections a node has decreases.

# Micro vs. Macro

Looking at the degree of an individual node, and comparing it against others in the network, is useful for finding out about the relative position of that node within the network. Looking at the degree *of every node at once* turns out to be exceptionally useful for talking about the network as a whole, and comparing it to others. I’ll leave a thorough discussion of degree distributions for a later post, but it’s worth mentioning them in brief here. The degree distribution shows how many nodes have how many edges.

As it happens, many real world networks exhibit something called “power-law properties” in their degree distributions. What that essentially means is that a small number of nodes have an exceptionally high degree, whereas most nodes have very low degrees. By comparing the degree distributions of two networks, it is possible to say whether they are structurally similar. There’s been some fantastic work comparing the degree distribution of social networks in various plays and novels to find if they are written or structured similarly.

# Extending Degree

For the entirety of this post, I’ve been talking about networks that were unweighted and undirected. Every edge counted just as much as every other, and they were all *symmetric* (a connection from A to B implies the same connection from B to A). Degree can be extended to both weighted and directed (*asymmetric*) networks with relative ease.

Combining degree with edge weights is often called *strength*. The strength of a node is the sum of the weights of its edges. For example, let’s say Steve is part of a weighted social network. The first time he interacts with someone, an edge is created to connect the two with a weight of 1. Every subsequent interaction incrementally increases the weight by 1, so if he’s interacted with Sally four times, Samantha two times, and Salvador six times, the edge weights between them are 4, 2, and 6 respectively.

In the above example, because Steve is connected to three people, his degree is **3**. Because he is connected to one of them four times, another twice, and another six times, his weight is 4+2+6=**8**.

Combining degree with directed edges is also quite simple. Instead of one degree score, every node now has two different degrees: *in-degree* and *out-degree*. The in-degree is the number of edges pointing to a node, and the out-degree is the number of edges pointing away from it. If Steve *borrowed* money from Sally, and *lent* money to Samantha and Salvador, his in-degree might be **1 **and his out-degree **2**.

# Powerful Degrees

The degree of a node is really very simple: more connections, higher degree. However, this simple metric accounts for quite a great deal in network science. Many algorithms that analyze both node-level properties and network-level properties are closely correlated with degree and degree distribution. This is a pareto-like effect; a great deal about a network is driven by the degree of its nodes.

While degree-based results are often intuitive, it is worth pointing out that the prime importance of degree is a direct result of the binary network representation of nodes and edges. Interactions either happen or they don’t, and everything that *is* is a self-contained node or edge. Thus, how many nodes, how many edges, and which nodes have which edges will be the driving force of any network analysis. This is both a limitation and a strength; basic counts influence so much, yet they are apparently powerful enough to yield intuitive, interesting, and ultimately useful results.

## 11 replies on “Networks Demystified 2: Degree”

[…] Weingart, Networks Demystified 2: Degree, December 17, […]

I’m really enjoying this series and I’m very surprised nobody else has commented on either of these articles yet, unless I’m totally missing the comments. Anyway I’m just learning about these topics myself as I’m learning (and blogging as I learn) about SNA.

On that note I was going to write something on centrality for an upcoming blog post I’ve been planning on doing about mining twitter for influential people on a given topic. However I feel like you explained it with far more elegance than I could. I’m going to link back here instead for the explanation.

So I just wanted to say keep up the good work, and you definitely have at least one new RSS subscriber and twitter follower thanks to this series.

Scott,

Thank you for writing a clear explanation to this topic. I’ve just completed Andre Skupin’s Knowledge Visualization seminar at SDSU. Network analysis can be a rather obtuse subject for the uninitiated. Your post on node degree is brief, to the point, and clearly written. But most important, it clarified this topic for me.

Dennis

Thanks, Dennis! I’m glad the post helped. Please send Andre my regards the next time you see him.

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Hello,

Thank you for your article, do you mind if I ask you a question about the calculation part?

I am working on a weighted directed graph or a network. I have self loops in it and I would like to know, when calculating the node strength (the sum of edges’s weighs going from and coming to a focal node (FN)) how to include self loops weigh ? should I count it twice?

I am using qgraph package and in that package the self loop is not included in calculating out degree and in degree so I need to do it by hand following this equation:

Node strength (FN) = (out-degree +self loop weigh) + (in-degree +self loop weigh)

it is correct and is there any reference to cite?

Thanks in advance

Laila

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