Local Access to Sparse Connected Subgraphs Via Edge Sampling

TL;DR

This paper presents a novel local algorithm for extracting sparse, connected subgraphs from dense graphs, balancing probe complexity and subgraph size, applicable to massive networks without restrictive assumptions.

Contribution

It introduces the first general-graph algorithm for local subgraph extraction with a tunable tradeoff between probes and subgraph edges, using innovative edge sparsification techniques.

Findings

Achieves subgraph with O(|V|T) edges using rom|E|/T probes

Works on general graphs without restrictive assumptions

Uses novel edge sparsification methods for local access

Abstract

We contribute an approach to the problem of locally computing sparse connected subgraphs of dense graphs. In this setting, given an edge in a connected graph $G = (V, E)$, an algorithm locally decides its membership in a sparse connected subgraph $G^* = (V, E^*)$, where $E^* \subseteq E$ and $|E^*| = o(|E|)$. Such an approach to subgraph construction is useful when dealing with massive graphs, where reading in the graph's full network description is impractical. While most prior results in this area require assumptions on $G$ or that $|E'| \le (1+\epsilon)|V|$ for some $\epsilon > 0$, we relax these assumptions. Given a general graph and a parameter $T$, we provide membership queries to a subgraph with $O(|V|T)$ edges using $\widetilde{O}(|E|/T)$ probes. This is the first algorithm to work on general graphs and allow for a tradeoff between its probe complexity and the number of edges in the resulting subgraph. We achieve this result with ideas motivated from edge sparsification techniques that were previously unused in this problem. We believe these techniques will motivate new algorithms for this problem and related ones. Additionally, we describe an efficient method to access any node's neighbor set in a sparsified version of $G$ where each edge is deleted with some i.i.d. probability.