Suppose we have a signed graph \(G = (V, E)\) where each node represents a member in a parliament. All members of the parliament will vote up (1) or down (0) on some measure. Every edge is annotated with either \(+\) or \(-\), indicating whether two members prefer to agree (\(+\)) or disagree (\(-\)) with each other on their positions. If you'd like, imagine that two members who are fighting prefer to disagree on their votes, while two strong friends prefer to agree on their votes. The goal of the fundamental cycle balancing problem is to assign each node a 0 / 1 label such that all of the edge constraints on agreement / disagreement are respected.
Background on Cycle Balancing
We can easily construct graphs that are impossible to label without violating at least one edge constraint (consider a triangle graph with exactly one negative edge). In practice, we settle on minimizing the number of violated constraints. The minimum number of violated constraints for a signed graph (across all possible labellings) is called the frustration index. Finding the exact value of the frustration index turns out to be NP-hard. We define a balanced graph as a signed graph where all edge constraints are satisfied, so the frustration index is the minimum number of edges whose signs we must flip to obtain a balanced state.
A 2021 Supercomputing paper (Alabandi et al.) does a pretty good job introducing this problem. It shifts the goalposts a bit; instead of computing the frustration index, it seeks to sample the frustration cloud, the set of minimally balanced states of the graph. A signed graph with some subset of edges \(Q\) with flipped labels is called minimally balanced iff:
- The graph is balanced
- No strict subset \(P \subset Q\) of those edges flipped in the original graph yields a balanced graph.
Generating a minimally balanced state is simple: pick any spanning tree of the graph and declare that all edges in that spanning tree must satisfy their edge constraints. Then loop through all remaining edges of the graph to determine whether their constraints are satisfied or violated. The SC21 paper proposes GraphB+, an parallel algorithm to sample the frustration cloud. It samples spanning trees on several GPUs and uses a traversal algorithm to identify the number of constraints violated by obeying all constraints in each tree.
The GraphB+ algorithm is not optimal. Here are some notes:
Once the GraphB+ algorithm samples a spanning tree, it does more work than necessary to identify violated constraints. Once we declare that every constraint in the spanning tree is obeyed, we can label each node to respect those choices by arbitrarily assigning 0 to the root and traversing the rest of the tree. From there, it suffices to loop over all edges not in the tree, read off the labels of their endpoints (along with the sign of the edge), and count up the number of violated constraints. GraphB+, by contrast, uses a more complicated cycle traversal algorithm to identify each violated constraint, along with potentially unecessary metadata on each vertex.
Is the frustration cloud just a tractable approximation to the frustration index (by averaging over the frustration values of samples)? Interestingly, the algorithm used to generate the spanning trees (see this other paper on fundamental cycle balancing) appears to affect the results. Can we develop algorithms to compute an approximate frustration index that do not rely on the frustration cloud?
In the remainder of this post, we will abandon the frustration cloud and consider efficient algorithms to compute the frustration index directly.
Computing the frustration index is really a special case of the graph bi-partitioning problem with binary \(+1\) / \(-1\) weighted edges. The similarity is obvious: we want to develop a cut in the graph such that the minimum number of \(+\) edges cross the cut. Here are a couple of major differences:
a. Most formulations of graph partitioning (and most software packages for graph partitioning, such as METIS) do not allow negative weight edges, whereas we require them for fundamental cycle balancing. In other words, we need to discourage some edges from crossing the cut while encouraging others to cross the cut.
b. Graph bipartitioning typically aims to equalize the sum of vertex weights in each side of the partition, whereas no such constraint exists in fundamental cycle balancing (although one could be introduced)
Relation to Graph Partitioning
Why can't we use a graph partitioner to solve this problem? Fair question; given how similar this problem is to graph partitioning, we might take a signed graph, assign weights to the edges according to some rule, and run a graph partitioner to bisect the graph. The issue here? It is difficult (maybe impossible! I'm thinking about proving it) to construct a weight assignment to the edges so that the graph partitioner optimizes an objective consistent with the cycle balancing problem.
Suppose we tried the following mapping: given a signed graph with edges labelled \(-1\) or \(+1\), replace the signs according to the map
\[-1 \mapsto 1\]
\[+1 \mapsto 2\]
The hope is that the graph partitioner will penalize the original \(+1\) edges that cross the cut disproportionately compared to the original negative weight edges. Then consider the following signed graph on four vertices shaped like the letter "N":
The frustration index of this graph is 0: we assign 1 to \(B\) and \(C\), and 0 to \(A\) and \(D\), corresponding to a partition \(P^*= [(B, C), (A, D)]\).
Now let's take a look at our graph partitioner's behavior under this mapping. \(P^*\) has two edges of weight +1 under our mapping that cross the cut, yielding a cost of 2. Now consider the alternate partition \(\hat P = [(A, B), (C, D)]\). To our graph partitioner, \(\hat P\) also has a cost of 2, since the positive weight edge crossing the partition has a cost of 2 under our mapping. But the frustration of \(\hat P\) is 3, since every constraint is violated! In other words, our graph partitioner will not distinguish between these two partitions in the transformed problem, while one is clearly optimal in the original problem. Increasing the weight assigned to positive edges would cause other undesirable effects, such as biasing the partitioner to treat positive edge constraints as more important than the negative ones.
Computing the Frustration Index
From the counterexample above, existing graph partitioning software might not accurately capture the frustration index. Still, we might be able to adapt techniques from the graph partitioning problem to solve our cycle balancing problem. These techniques include:
Kernighan-Lin or Fiduccia-Mattheyses Heuristics: Start with some partition and iteratively refine it according to some objective function
Spectral clustering: Find the second smallest eigenvalue / eigenvector pair of the graph Laplacian. This paper appropriately modifies the graph Laplacian to account for negative weight edges in signed graphs.
Hierarchical Coursening / Refinement: Solve the problem on a coursened approximation of the graph, then refine the solution successively at finer resolutions
You could also attempt to sample spanning trees, as GraphB+ does, without the baggage of counting violated constraints by cycle traversal mentioned above. An approximate frustration index could be computed by averaging the frustrations induce by each of the trees.
We want these these techniques to be parallelizable so we can analyze graphs with millions of vertices and billions of edges.
CS267 Class Projects!
I outlined some of these ideas to students in the 2022 version of CS267: Applications of Parallel Computers. Several of them chose to work on this problem for their final projects! I hope it made for fun and interesting work.