Recognizing Weak Embeddings of Graphs
Hugo A. Akitaya, Radoslav Fulek, Csaba D. Tóth · Society for Industrial and Applied Mathematics eBooks · 2018
We present an efficient algorithm for a problem in the interface between clustering and graph embeddings. An embedding φ : G → M of a graph G into a 2-manifold M maps the vertices in V (G) to distinct points and the edges in E (G) to interior-disjoint Jordan arcs between the corresponding vertices. In applications in clustering, cartography, and visualization, nearby vertices and edges are often bundled to a common node or arc, due to data compression or low resolution. This raises the computational problem of deciding whether a given map φ : G → M comes from an embedding. A map φ : G → M is a weak embedding if it can be perturbed into an embedding ψε : G → M with ║φ – ψε║ 0. A polynomial-time algorithm for recognizing weak embeddings was recently found by Fulek and Kynčl [14], which reduces to solving a system of linear equations over ℤ2. It runs in O(π2ω) ≤ O(n4.75) time, where ω ≈ 2.373 is the matrix multiplication exponent and n is the number of vertices and edges of G. We improve the running time to O(n log n). Our algorithm is also conceptually simpler than [14]: We perform a sequence of local operations that gradually “untangles” the image φ(G) into an embedding ψ(G), or reports that φ is not a weak embedding. It generalizes a recent technique developed for the case that G is a cycle and the embedding is a simple polygon [1], and combines local constraints on the orientation of subgraphs directly, thereby eliminating the need for solving large systems of linear equations.