Efficiently Estimating Motif Statistics of Large Networks

TL;DR

This paper introduces sampling algorithms that efficiently estimate network motif statistics in large networks with minimal queries, outperforming existing methods in accuracy and query efficiency.

Contribution

The work presents novel sampling algorithms that require no full network knowledge and provide theoretical guarantees, significantly reducing query complexity for motif estimation.

Findings

Algorithms require fewer queries than state-of-the-art methods.

The methods have theoretical convergence guarantees.

Experimental results show high accuracy with minimal sampling.

Abstract

Exploring statistics of locally connected subgraph patterns (also known as network motifs) has helped researchers better understand the structure and function of biological and online social networks (OSNs). Nowadays the massive size of some critical networks -- often stored in already overloaded relational databases -- effectively limits the rate at which nodes and edges can be explored, making it a challenge to accurately discover subgraph statistics. In this work, we propose sampling methods to accurately estimate subgraph statistics from as few queried nodes as possible. We present sampling algorithms that efficiently and accurately estimate subgraph properties of massive networks. Our algorithms require no pre-computation or complete network topology information. At the same time, we provide theoretical guarantees of convergence. We perform experiments using widely known data sets, and show that for the same accuracy, our algorithms require an order of magnitude less queries (samples) than the current state-of-the-art algorithms.