Computing Graph Descriptors on Edge Streams

TL;DR

This paper introduces scalable streaming algorithms for computing graph descriptors directly from edge streams, enabling efficient analysis of large graphs with controlled runtime and memory, while maintaining high classification accuracy.

Contribution

The paper presents novel streaming algorithms for approximate graph descriptor computation that scale to large graphs and control runtime and memory usage.

Findings

Descriptors computed within minutes for graphs with millions of edges.

Achieves similar classification accuracy to state-of-the-art methods.

Uses only 25% of the memory required by traditional algorithms.

Abstract

Feature extraction is an essential task in graph analytics. These feature vectors, called graph descriptors, are used in downstream vector-space-based graph analysis models. This idea has proved fruitful in the past, with spectral-based graph descriptors providing state-of-the-art classification accuracy. However, known algorithms to compute meaningful descriptors do not scale to large graphs since: (1) they require storing the entire graph in memory, and (2) the end-user has no control over the algorithm's runtime. In this paper, we present streaming algorithms to approximately compute three different graph descriptors capturing the essential structure of graphs. Operating on edge streams allows us to avoid storing the entire graph in memory, and controlling the sample size enables us to keep the runtime of our algorithms within desired bounds. We demonstrate the efficacy of the proposed descriptors by analyzing the approximation error and classification accuracy. Our scalable algorithms compute descriptors of graphs with millions of edges within minutes. Moreover, these descriptors yield predictive accuracy comparable to the state-of-the-art methods but can be computed using only 25% as much memory.