Power method and centrality

A few days ago I wrote about eigenvector centrality, a way of computing which nodes in a network are most important. Rather than simply looking for the most highly connected nodes, it looks for nodes that are highly connected to nodes that are highly connected. It’s the idea behind Google’s PageRank algorithm.

Adjacency matrices

One way to capture the structure of a graph is its adjacency matrix A. Each element aij of this matrix equals 1 if there is an edge between the ith and jth node and a 0 otherwise. If you square the matrix A, the (i, j) entry of the result tells you how many paths of length 2 are between the ith and jth nodes. In general, the (i, j) entry of An tells you how many paths of length n there are between the corresponding nodes.

Power method

Calculating eigenvector centrality requires finding an eigenvector associated with the largest eigenvalue of A. One way to find such an eigenvector is the power method. You start with a random initial vector and repeatedly multiply it by A. This produces a sequence of vectors that converges to the eigenvector we’re after.

Conceptually this is the same as computing An first and multiplying it by the random initial vector. So not only does the matrix An count paths of length n, for large n it helps us compute eigenvector centrality.

Theoretical details

Now for a little fine print. The power method will converge for any starting vector that has some component in the direction of the eigenvector you’re trying to find. This is almost certainly the case if you start with a vector chosen at random. The power method also requires that the matrix has a single eigenvector of largest magnitude and that its associated eigenspace have dimension 1. The post on eigenvector centrality stated that these conditions hold, provided the network is connected.

Computational practicalities

In principle, you could use calculate eigenvector centrality by computing An for some large n. In practice, you’d never do that. For a square matrix of size N, a matrix-vector product takes O(N²) operations, whereas squaring A requires O(N³) operations. So you would repeatedly apply A to a vector rather than computing powers of A.

Also, you wouldn’t use the power method unless A is sparse, making it relatively efficient to multiply by A. If most of the entries of A are zeros, and there is an exploitable pattern to where the non-zero elements are located, you can multiply A by a vector with far less than N² operations.

Eigenvector centrality

A basic question to ask about a network is which nodes are most important. A simple way of answering this question would be to say the most well-connected nodes, i.e. the nodes with the most edges. This approach is known as degree centrality. It’s not a bad place to start. It’s easy to understand and easy to compute. It may be adequate for some purposes, but it’s often helpful to take a more sophisticated approach known as eigenvector centrality.


Let’s look at a few motivating examples before we get into the details.

Social network marketing

If you wanted to advertise something, say a book you’ve written, and you’re hoping people will promote it on Twitter. Would you rather get a shout out from someone with more followers or someone with less followers? All else being equal, more followers is better. But even better would be a shout out from someone whose followers have a lot of followers.

Deans and flight attendants

Suppose you’re at a graduation ceremony. Your mind starts to wander after the first few hundred people walk across the stage, and you start to think about how a cold might spread through the crowd. The dean handing out diplomas could be a superspreader because he’s shaking hands with everyone as they receive their diplomas. But an inconspicuous parent in the audience may also be a superspreader because she’s a flight attendant and will be in a different city every day for the next few days. And not only is she a traveler, she’s in contact with travelers.

Search engines

Ranking web pages according to the number of inbound links they have was a good idea because this takes advantage of revealed preferences: instead of asking people what pages they recommend, you can observe what pages they implicitly recommend by linking to them. An even better idea was Page Rank, weighing inbound links by how many links the linking pages have.

Eigenvector centrality

The idea of eigenvector centrality is to give each node a rank proportional to the sum of the ranks of the adjacent nodes. This may seem circular, and it kinda is.

To know the rank of a node, you have to know the ranks of the nodes linking to it. But to know their ranks, you have to know the ranks of the nodes linking to them, etc. There is no sequential solution to the problem because you’d end up in an infinite regress, even for a finite network. But there is a simultaneous solution, considering all pages at once.

You want to assign to the ith node a rank xi proportional to the sum of the ranks of all adjacent nodes. A more convenient way to express this to compute the weighted sum of the ranks of all nodes, with weight 1 for adjacent nodes and weight 0 for non-adjacent nodes. That is, you want to compute

x_i = \frac{1}{\lambda} \sum a_{ij} x_j

where the a‘s are the components of the adjacency matrix A. Here aij equals 1 if there is an edge between nodes i and j and it equals 0 otherwise.

This is equivalent to looking for solutions to the system of equations

Ax = \lambda x

where A is the adjacency matrix and x is the vector of node ranks. If there are N nodes, then A is an N × N matrix and x is a column vector of length N.

In linear algebra terminology, x is an eigenvector of the adjacency matrix A, hence the name eigenvector centrality.

Mathematical questions

There are several mathematical details to be concerned about. Does the eigenvalue problem defining x have a solution? Is the solution unique (up to scalar multiples)? What about the eigenvalue λ? Does the adjacency matrix have multiple eigenvalues, which would mean there are multiple eigenvectors?

If A is the adjacency matrix for a connected graph, then there is a unique eigenvalue λ of largest magnitude, there is a unique corresponding eigenvector x, and all the components of x are non-negative. It is often said that this is a result of the Perron–Frobenius theorem, but there’s a little more to it than that because you need the hypothesis that the graph is connected.

The matrix A is non-negative, but not necessarily positive: some entries may be zero. But if the graph is connected, there’s a path between any node i and another node j, which means for some power of A, the ij entry of A is not zero. So although A is not necessarily positive, some power of A is positive. This puts us in the positive case of the Perron–Frobenius theorem, which is nicer than the non-negative case.

This section has discussed graphs, but Page Rank applies to directed graphs because links have a direction. But similar theorems hold for directed graphs.

Related posts

Coloring the queen’s graph

Suppose we have an n × n chessboard. The case n = 8 is of course most common, but we consider all positive integer values of n.

The graph of a chess piece has an edge between two squares if and only if the piece can legally move between the two squares.

Now suppose we have a rule that if a piece can move between two squares, the squares must be colored differently. The minimum number of colors necessary to color the chessboard this was is the chromatic number of that piece’s graph.

The chromatic number of a rook is clearly at least n: since a rook can move between any two squares in a row, every square in the row must be a different color. And in fact the chromatic number for a rook’s graph is exactly n.

Bishops are a little more complicated. If n is even, then the chromatic number for a bishop is n. If n is odd, the number is n for a white bishop but n − 1 for a black bishop. Thinking of a 3 × 3 board shows why the two bishops are different.

Now a queen is sorta like a rook and a bishop combined, but determining the chromatic number for a queen’s graph is more difficult. The chromatic number of a queen can be no less than that of a rook since the former can make all the moves of the latter. So the queen’s chromatic number is at least n, but is it equal to n?

The results stated above can be found in [1].

Let k(n) be the chromatic number of a queen’s graph on an the n × n chessboard. The results in [1] regarding k(n) are summarized as follows.

  • k(1) = 1
  • k(2) = 4
  • k(3) = k(4) = 5
  • k(6) = 7
  • k(n) = n if n is not divisible by 2 or 3.

The authors stated that they had found an example proving that k(8) ≤ 9 and conjectured that k(8) = 9. You can find a C++ program that confirms this conjecture here.

The authors conjectured that k(n) ≤ n + 1 for all n > 3. I don’t know whether this has been proven. My impression following a brief search is that the problem of finding k(n) for all n is still not fully resolved.

Update: According to a link in the comments, n = 27 is the smallest n such that k(n) is unknown.

Related posts

[1] M. R. Iyer and V. V. Menon. On Coloring the n × n Chessboard. The American Mathematical Monthly, 1966, Vol. 73, pp. 721–725.

From graph theory to category theory

Let G be a directed graph whose nodes are the positive integers and whose edges represent relations between two integers. In our first example we’ll draw an edge from x to y if x is a multiple of y. In our second example we’ll draw an edge from x to y if xy.

In both examples we define a function p(x, y) to be the unique node such that whenever a node z has directed edges going to x and to y, there is also a directed node from z to p(x, y).

Multiplication example

In this example there will be an edge from x to y if (and only if) x is a multiple of y. So, for instance, there is an edge from every even number to 2. There are edges from 15 to 1, 3, 5, and 15.

Now suppose there is some node with edges to 6 and 7. Call this node p(6, 7) or just p. Then p must be some multiple of 6 and 7. Also, by our definition of p we know that if there is an edge from any node z to 6 and 7, there must also be an edge from z to p. This says every multiple of 6 and 7 must also be a multiple of p. So p = 42. The node labeled z could be 4200, for example, but p can only be 42.

To generalize from this example, the node p(x, y) is the least common multiple of x and y.

Order example

In this example there will be an edge from x to y if and only if xy. Every positive integer points to itself and to every smaller integer.

Now what would p(x, y) be? It’s something no less than x and no less than y. And by definition of p, every number greater than p(x, y) is at least as big as p(x, y). That is, p(x, y) is the smallest integer no less than x or y, i.e. the maximum of x and y.

The reveal

The integer p(x, y) is the product of x and y in the sense of category theory. It may also be the product in the basic sense of multiplying two numbers together, but it might not be. The definition in terms of nodes and edges generalizes the notion of product, so that the familiar product is an example, the canonical example, but not the only example.

The category theory notion of a product abstracts something that multiplication and maximum have in common. More on this here.

We could go back and define a function c(x, y) by saying replacing “to” with “from” in the definition of p. That is, c(x, y) is the unique node such that whenever a node z has directed edges coming from x and from y, there is also a directed node to z coming from c(x, y). The function c is the coproduct.

Emphasizing the edges

In category theory, definitions, such as the definition of product and coproduct, depend not just on the objects but on the morphisms between them. In graph theory language, definitions depend not just on the nodes but also on the edges. Keep the same objects but define different morphisms and you get different products, as we did above.

Often there is a standard set of morphisms (edges), so standard that they are often left implicit. That’s usually OK, but sometimes the morphisms need to be emphasized, either because they are not the usual morphisms or because we need to stress some property of the morphisms. Morphisms are typically structure-preserving functions, and we may need to emphasize the structure-preserving part.

Creating a Traveling Salesman Tour of Texas with Mathematica

A Traveling Salesman tour visits a list of destinations using the shortest path. There’s an obvious way to find the shortest path connecting N points: try all N! paths and see which one is shortest. Unfortunately, that might take a while.

Texas has 254 counties, and so calculating a tour of Texas counties by brute force would examine 254! paths, over 10500 paths. In theory, large Traveling Salesman problems are unsolvable. In practice they can often be solved quickly. As is often the case, the key is to give yourself just a little slack and look for solutions that are close to optimal.

I’ve used the example of a Traveling Salesman tour of Texas before because it makes a nice visual. People asked me for the code that made the image, but I didn’t save the code and didn’t remember offhand how to re-create it. So here’s the code for future reference.

Incidentally, computing the tour itself took only a second or two. Creating the visualization took several seconds.

    texas = Entity["AdministrativeDivision", "Texas"]; 
    counties = texas["Subdivisions"];
    tour = FindShortestTour[texas["Subdivisions"]];
    GeoGraphics[{Thick, Red, GeoPath[counties[[tour[[2]]]]]}]

Here counties is a list of objects representing Texas counties, sorted by alphabetical order, from Anderson County to Zavala County.

The tour object is a pair of a distance and a list of integers. The distance, 6780.74 nautical miles, is the length of the tour. The integers are the indexes of the counties in the tour.

{6780.74 nmi, {1, 107, 234, …, 201, 37, 1}}

The tour starts with the first county, Anderson County. It’s got to start somewhere, and I expect it always starts with the first item in the list. Next it goes to the 107th county, Henderson County, and so on. Because FindShortestTour returns a closed tour, the tour ends where it started, in Anderson County.

Related posts: Traveling Salesman tours of Africa, Americas, Eurasia and Oceania.

Topological sort

When I left academia [1] my first job was working as a programmer. I was very impressed by a new programmer we hired who hit the ground running. His first week he looked at some problem we were working on and said “Oh, you need a topological sort.” I’d never heard of a topological sort and it sounded exotic. What could this have to do with topology?!

A topological sort of a directed graph lists source nodes before target nodes. For example, if there is a directed edge from A to B and from C to A, then the nodes would be list C, A, B. It’s just a way of listing items in a directed graph so that no item in the list points to an item earlier in the list. All arrows point forward.

This is not exotic at all. It’s something you’ve likely done, maybe by hand. As pointed out in the comments, the make utility does this, compiling source files in the order that they’re needed [2].

Where does topology come in? Imagine your directed graph made of beads and strings. You want to pick up the graph by some bead so that all beads are higher than the beads they point to. It’s topological in the sense that you don’t need to preserve the geometry of the graph, only its connectivity.


The Unix utility tsort will do a topological sort. The input to the utility is a text file with two items per line, separated by white space, indicating a directed edge from the first item to the second.


Here is a thumbnail image of a graph of relationships between special functions. See this page for a full-sized image and an explanation of what the arrows represent.

special function relationships

I took the GraphViz file used to create the graph and formatted it for tsort. Then I randomly shuffled the file with shuf.

    Gegenbauer_polynomials Legendre_polynomials
    Gegenbauer_polynomials Chebyshev_polynomials_Second_kind
    Hypergeometric_2F1 Jacobi_polynomials
    Error_function Fresnel_S
    Hypergeometric_1F1 Error_function

The lines are not sorted topologically because, for example, the Gegenbauer polynomials are special cases of the Hypergeometric 2F1 functions, so Hypergeometric 2F1 should be listed before Gegenbauer polynomials.

When I ran the shuffled file through tsort I got


and now in this list more general functions always come before special cases.

Related posts

[1] After a postdoc at Vanderbilt, I took a job as a programmer. I got the job because they needed a programmer who knew some DSP. A few years later I got a job at MD Anderson Cancer Center managing a group of programmers. It’s fuzzy whether my time at MDACC should be considered time in Academia. My responsibilities there were sometimes academic—writing journal articles, teaching classes—and sometimes not—developing software and managing software developers.

[2] The make software can be used to run any directed acyclic graph of tasks, but is most often used to compile software.

The NBA and MLB trees are isomorphic

NBA MLB games

An isomorphism is a structure-preserving function from one object to another. In the context of graphs, an isomorphism is a function that maps the vertices of one graph onto the vertices of another, preserving all the edges.

So if G and H are graphs, and f is an isomorphism between G and H, nodes x and y are connected in G if and only if nodes f(x) and f(y) are connected in H.

There are 30 basketball teams in the National Basketball Association (NBA) and 30 baseball teams in Major League Baseball (MLB). That means the NBA and MLB are isomorphic as sets, but it doesn’t necessarily mean that the hierarchical structure of the two organizations are the same. But in fact the hierarchies are the same.

Both the NBA and MLB have two top-level divisions, each divided into three subdivisions, each containing five teams.

Basketball has an Eastern Conference and a Western Conference, whereas baseball has an American League and a National League. Each basketball conference is divided into three divisions, just like baseball leagues, and each division has five teams, just as in baseball. So the tree structures of the two organizations are the same.

In the earlier post about the MLB tree structure, I showed how you could number baseball teams so that the team number n could tell you the league, division, and order within a division by taking the remainders when n is divided by 2, 3, and 5. Because the NBA tree structure is isomorphic, the same applies to the NBA.

Here’s a portion of the graph with numbering. The full version is available here as a PDF.

Here’s the ordering.

  1. Los Angeles Clippers
  2. Miami Heat
  3. Portland Trail Blazers
  4. Milwaukee Bucks
  5. Dallas Mavericks
  6. Brooklyn Nets
  7. Los Angeles Lakers
  8. Orlando Magic
  9. Utah Jazz
  10. Chicago Bulls
  11. Houston Rockets
  12. New York Knicks
  13. Phoenix Suns
  14. Washington Wizards
  15. Denver Nuggets
  16. Cleveland Cavaliers
  17. Memphis Grizzlies
  18. Philadelphia 76ers
  19. Sacramento Kings
  20. Atlanta Hawks
  21. Minnesota Timberwolves
  22. Detroit Pistons
  23. New Orleans Pelicans
  24. Toronto Raptors
  25. Golden State Warriors
  26. Charlotte Hornets
  27. Oklahoma City Thunder
  28. Indiana Pacers
  29. San Antonio Spurs
  30. Boston Celtics

Incidentally, the images at the top of the post were created with DALL-E. They look nice overall, but you’ll see bizarre details if you look too closely.

A mathematical look at the NFL

This post will look at the National Football League through the lens of graph theory, topology, and binary numbers.

The NFL has a very nice tree structure, which isn’t too surprising in light of the need to make tournament brackets. The NFL is divided into two conferences, the American Football Conference and the National Football Conference.

Tree structure

Each conference is divided into four divisions named after geographical regions. Since this is a mathematical post, I’ve listed the regions counterclockwise starting in the east because that’s how mathematicians do things.

NFL -> AFC<br /> NFL -> NFC<br /> AFC -> AFCE<br /> AFC -> AFCN<br /> AFC -> AFCW<br /> AFC -> AFCS<br /> NFC -> NFCE<br /> NFC -> NFCN<br /> NFC -> NFCW<br /> NFC -> NFCS

Each division has four teams. Adding each team under its division would make an awkwardly wide graph. I made a graph of the entire tree, rotated so that image is long rather than wide. Here’s a little piece of it.

The full image is available here.


Now you may wonder how well the geographic division names correspond to geography. For example, the Dallas Cowboys are in the NFC East, and it’s a little jarring to hear Texas called “east.”

But within each conference, all the “East” teams are indeed east of all the West teams. And with one exception, all the North teams are indeed north of the South teams. The Indianapolis Colts are the exception. The Colts are in the AFC South, but are located to the north of the Cincinnati Bengals and the Baltimore Ravens in the AFC North.

This geographical sorting only applies within a conference. The Dallas Cowboys, for example are east of all the West teams within their conference, but they are west of the Kansas City Chiefs in the AFC West.

Here’s where topology comes in: you can make the division names match their geography if you morph the map of the United States pulling Indianapolis south of its geometric location.

Binary numbers

The graph structure of the NFL is essentially a full binary tree; you could make it into a binary tree by introducing a sub-conference layer and grouping the teams into pairs.

You could number the NFL teams with five bits: one for the conference, two for the division, and two more for the team. We could make the leading bit 0 for the AFC and 1 for the NFC. Then within each division, we could use 00 for East, 01 for North, 10 for West, and 11 for South. As mentioned above, this follows the mathematical convention of angles increasing counterclockwise starting at the positive x-axis.

The table above is an SVG image; here is the same data in plain text.

Related posts

Visualizing correlations with graphs

Yesterday I found a statistics textbook for geologists [1] for $1 at a library book sale. When I thumbed through the book an image similar to the one below caught my eye.

This image approximates Figure 15.2 in [1],

The nodes represent six factors of the thickness of rock formations and the edges are labeled with the correlations between factors. Only large correlations are shown. For example, in theory everything is correlated with “total” but carbonates are not significantly correlated with the total. Nonclastics divide into evaporates and carbonates; apparently nearly all the nonclastics in this data set were evaporites.

Notice that this example illustrates that correlation is not transitive. That is, if A is correlated with B and B is correlated with C, it does not follow that A is necessarily correlated with C.

Making the graph

I made the graph above with GraphViz using the following code.

    graph G {
    T [label="Total"      , pos="2.50, 5.00!"]
    S [label="Sand"       , pos="4.66, 3.75!"]
    C [label="Carbonates" , pos="4.66, 1.25!"]
    E [label="Evaporites" , pos="2.50, 0.00!"]
    N [label="Nonclastics", pos="0.39, 1.25!"]
    H [label="Shale"      , pos="0.39, 3.75!"]
    T -- S [label=" 0.24 "]
    T -- H [label=" 0.89 "]
    T -- N [label=" 0.84 "]
    T -- E [label=" 0.82 "]
    H -- N [label=" 0.69 "]
    H -- E [label=" 0.70 "]
    S -- C [label=" 0.45 "]
    N -- E [label=" 0.99 "]


I’ve mostly used GraphViz to make graphs when I didn’t care much about the layout. I’ve experimented with a few layout engines, but I hadn’t tried specifying the node positions before.

The nodes in the original graph were arranged in a circle, so I tried the circo layout engine. This did not position the nodes in a circle. I also tried specifying the positions without the bang on the end, giving the positions as layout hints. GraphViz did not appreciate my suggestions and was certain that it knew better how to layout the graph. But when I added the exclamation marks GraphViz acquiesced to my wishes.

GraphViz will create output in a variety of formats. I tried PNG and SVG. The SVG image above was 11 times smaller than the PNG output. One reason I starting using SVG images more often is that they often result in smaller files. They also look very nice at multiple resolutions, i.e. on a desktop and on a mobile device.

Related posts

[1] Krumbein and Graybill. An Introduction to Statistical Models in Geology. McGraw-Hill, 1965.

Graphing Japanese Prefectures

The two previous posts looked at adjacency networks. The first used examples of US states and Texas counties. The second post made suggestions for using these networks in a classroom. This post is a continuation of the previous post using examples from Japan.

Japan is divided into 8 regions and 47 prefectures. Here is a network diagram of the prefectures in the Kanto region showing which regions border each other. (In this post, “border” will be regions share a large enough border that I was able to see the border region on the map I was using. Some regions may share a very small border that I left out.)

This is a good example of why it is convenient in GraphViz to use variable names that are different from labels. I created my graphs using English versions of prefecture names, and checked my work using the English names. Then after debugging my work I changed the label names (but not the connectivity data) to use Japanese names.

To show what this looks like, my GraphViz started out like this

    graph G {
    AI [label="Aichi"]
    AK [label="Akita"]
    AO [label="Aomori"]
    AO -- AK
    AO -- IW
    AK -- IW

and ended up like this

    graph G {
    AI [label="愛知県"]
    AK [label="秋田県"]
    AO [label="青森県"]
    AO -- AK
    AO -- IW
    AK -- IW

Here’s a graph only showing which prefectures border each other within a region.

This image is an SVG, so you can rescale it without losing any resolution. Here’s the same image as a PDF.

Because this network is effectively several small networks, it would be easy to look at a map and figure out which nodes correspond to which prefectures. (It would be even easier if you could read the labels!)

Note that there are two islands—literal islands, as well as figurative islands in the image above—Hokkaido, which is its own region, and Okinawa, which a prefecture in the Kyushu region.

Here’s the graph with all bordering relations, including across regions.

The image above is also an SVG. And here’s the same image as a PDF.