Hedgegraph Polymatroids
Karthekeyan Chandrasekaran, Chandra Chekuri, Weihang Wang, Weihao Zhu
TL;DR
This work develops a polymatroid-based framework to study connectivity in hedgegraphs, a generalization of hypergraphs that groups hyperedges into hedges to model interdependencies. By defining the hedgegraph polymatroid $f_G(A)=|V|- \\#Comps(V,A)$ and partition-based connectivity measures, the authors obtain a suite of structural and algorithmic results, including a polynomial-time computation of partition connectivity and a min-max decomposition theorem via hedgegraph matroids. They show that hedgegraphs are k-partition connected iff they contain k hedge-disjoint 1-partition connected sub-hedgegraphs, with trimming-based spanning-tree characterizations and orientation consequences. Additional contributions include the notions of weak partition connectivity, sampling guarantees that preserve connectivity, and partition sparsifiers derived from polymatroid quotient sparsification, all generalizing classical graph/hypergraph results to hedges. The collective framework provides principled, tractable avenues for understanding connectivity in hedged dependencies and offers potential practical impacts for reliability, decomposition, and sparsification in complex network models.
Abstract
Graphs and hypergraphs combine expressive modeling power with algorithmic efficiency for a wide range of applications. Hedgegraphs generalize hypergraphs further by grouping hyperedges under a color/hedge. This allows hedgegraphs to model dependencies between hyperedges and leads to several applications. However, it poses algorithmic challenges. In particular, the cut function is not submodular, which has been a barrier to algorithms for connectivity. In this work, we introduce two alternative partition-based measures of connectivity in hedgegraphs and study their structural and algorithmic aspects. Instead of the cut function, we investigate a polymatroid associated with hedgegraphs. The polymatroidal lens leads to new tractability results as well as insightful generalizations of classical results on graphs and hypergraphs.
