I feel bad for not having written a post in so long! I have been busy with teaching, research, and various other projects. Now that my teaching duties are done, I will try to post more regularly!
A graph is a collection of nodes that are connected by edges that are drawn under a defined condition. These edges may or may not have weights. Graphs are useful representations of data in many scenarios. For example, the Internet is an example of a graph. Each web page would be a node and an edge would be drawn between two nodes if one of the pages links to the other. These edges could be unweighted or could represent the number of links between the two pages. Another example is social media. For example, the nodes could be all users of the service and an edge would be drawn if two nodes are "friends" with each other.
One way to define a distance between two nodes on the graph may be to find the shortest path between the nodes. This would be defined as the collection of edges from one node to the other such that the sum of the weights of the edges (or simply the number of edges in the case of an unweighted graph) is minimized. This is what LinkedIn seems to do when they compute the "degree" of connection of a stranger to you. This is also how Bacon and Erdös numbers are defined.
A shortcoming of this measure, though, is that shortest path ignores how many paths there are from one node to the other. This scenario is depicted in Fig. 1. Suppose in a social network, we would like to determine something about how likely it is that one person (say Alice) will meet another (say Bob) in the future. Suppose Bob is a friend of only one of Alice's friends. Then, given no other information, it seems unlikely that Alice would ever meet Bob, since there is only one avenue for Alice to meet Bob. Of course, this could be different if Bob was good friends with Alice's significant other, so we might want to weight edges if we have information about how close Alice is to her friends in the social network. Now, if Bob is friends with half of Alice's friends, it seems quite likely that when Alice goes to a party with her friends or is hanging out with one of those friends, then Alice will run into Bob. In both of these cases, the shortest path distance between Alice and Bob is the same, but the outcome of how likely they are to meet (which is what we want to analyze) is different.
It turns out that a useful analogy can be made by considering each edge as a resistor in a resistor network. In the unweighted case, each edge is a resistor with resistance 1 (or if having a resistance without units bothers you, though units will be dropped for the rest of the post) and in the weighted case, each edge is a resistor with resistance equal to the inverse weight of the edge. We will see that the effective resistance between two nodes is a good measure of distance that more accurately captures the scenario described above. Groups of nodes with small effective resistance between them will correspond to clusters of people (such as people who work in one workplace) in the social network.
The effective resistance satisfies the triangle inequality, which is a defining property of distances . We can see this as follows. The effective resistance between nodes and is the voltage between the two nodes when one unit of current is sent into and extracted from Let the voltage at be zero (we are always free to set zero potential wherever we like). Then Now, we know that since the potential should be maximal at the source (current doesn't climb uphill). Similarly, (remember node is grounded). Thus, we have that and the triangle inequality holds. The other defining characteristics hold quite trivially, so the effective resistance is a reasonable way to calculate distance.
Now consider the example presented before and depicted in Fig. 1. If Bob is only friends with one of Alice's friends, and there are no other links between Alice and Bob, then the effective resistance between Alice and Bob is 2. In this case, the effective resistance is the same as the shortest path distance. If Bob is friends with 7 of Alice's friends, and there are no other links between Alice and Bob, the effective resistance between Alice and Bob is 0.29. So, we see that this satisfies the property that having one connection is not as important as having many connections to Bob.
Another interesting consequence of this analogy is related to random walks. Suppose a walker starts at a node , and chooses a random edge connected to that node to walk along (the random choice is weighted by edge weights if the graph is weighted). Then, the expected number of steps to start at go to another node and return back to (which is called the commute time) is proportional to the effective resistance between and One way to think about this is that a way to estimate effective resistance, then, on a large resistor network would be through the use of random walks . This is interesting for tricky resistor problems such as the infinite grid of resistors. This also reinforces the idea that effective resistance is a good way of quantifying communities. When one node is well connected with another, it should be relatively easy to commute between the nodes, and thus they should be part of the same community. Further, this notion can be used to place bounds on the maximum possible commute time.
Effective resistance has many other uses as well. Quickly computing effective resistance turns out to be quite useful in speeding up an algorithm that solves the maximum flow problem . In addition, sampling edges by weights proportional to the effective resistance across the edge yields an algorithm that sparsifies graphs. This means that the new graph has fewer nodes and edges than the original graph, but looks "close" to the original graph, and is a more efficient representation of that information .
1. Lau, L.C., 2015. Lecture notes on electrical networks.
2. Ellens, W., 2011. Effective Resistance.
3. Christiano, P., et al., 2010. Electrical Flows, Laplacian Systems, and Faster Approximation of Maximum Flow in Undirected Graphs.
4. Spielman, D. and Srivastava, N., 2011. Graph Sparsification by Effective Resistances. SIAM J. Comput., 40(6), 1913–1926.