The Expander Hierarchy and its Applications to Dynamic Graph Algorithms

Gramoz Goranci, Harald Räcke, Thatchaphol Saranurak, Zihan Tan

Abstract: We introduce a notion for hierarchical graph clustering which we call the expander hierarchy and show a fully dynamic algorithm for maintaining such a hierarchy on a graph with n vertices undergoing edge insertions and deletions using no(1) update time. An expander hierarchy is a tree representation of graphs that faithfully captures the cut-flow structure and consequently our dynamic algorithm almost immediately implies several results including: The first fully dynamic algorithm with no(1) worst-case update time that allows querying no(1)-approximate conductance, s-t maximum flows, and s-t minimum cuts for any given (s, t) in O(log1/6 n) time. Our results are deterministic and extend to multi-commodity cuts and flows. All previous fully dynamic (or even decremental) algorithms for any of these problems take Ω(n) update or query time. The key idea behind these results is a fully dynamic algorithm for maintaining a tree flow sparsifier, a notion introduced by Räcke [FOCS'02] for constructing competitive oblivious routing schemes. A deterministic fully dynamic connectivity algorithm with no(1) worst-case update time. This significantly simplifies the recent algorithm by Chuzhoy et al. that uses the framework of Nanongkai, Saranurak, and Wulff-Nilsen [FOCS'17]. A deterministic fully dynamic treewidth decomposition algorithm on constant-degree graphs with no(1) worst-case update time that maintains a treewidth decomposition of width tw(G) · no(1) where tw(G) denotes the treewidth of the current graph. This is the first non-trivial dynamic algorithm for this problem. Our technique is based on a new stronger notion of the expander decomposition, called the boundary-linked expander decomposition. This decomposition is more robust against updates and better captures clustering structure of graphs compared to the standard expander decomposition. Given that the expander decomposition has proved extremely useful in many fields, including approximation, sketching, distributed, and dynamic algorithms, we expect that our new notion will find more future applications.