Sampling Multiple Nodes in Large Networks: Beyond Random Walks

TL;DR

This paper introduces a novel sampling method for large networks that reduces query complexity by explicitly exploring less accessible network components, outperforming traditional random walk approaches.

Contribution

It presents a new sampling technique that bypasses mixing time dependence, enabling efficient large-scale network sampling with fewer queries.

Findings

Query complexity improved up to 20 times.

Effective on networks with tens of millions of nodes.

Outperforms existing random walk-based methods.

Abstract

Sampling random nodes is a fundamental algorithmic primitive in the analysis of massive networks, with many modern graph mining algorithms critically relying on it. We consider the task of generating a large collection of random nodes in the network assuming limited query access (where querying a node reveals its set of neighbors). In current approaches, based on long random walks, the number of queries per sample scales linearly with the mixing time of the network, which can be prohibitive for large real-world networks. We propose a new method for sampling multiple nodes that bypasses the dependence in the mixing time by explicitly searching for less accessible components in the network. We test our approach on a variety of real-world and synthetic networks with up to tens of millions of nodes, demonstrating a query complexity improvement of up to $\times 20$ compared to the state of the art.