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Learning to count: A deep learning framework for graphlet count estimation

Published online by Cambridge University Press:  11 September 2020

Xutong Liu Open the ORCID record for Xutong Liu [Opens in a new window] ,
Yu-Zhen Janice Chen ,
John C. S. Lui  and
Konstantin Avrachenkov
Xutong Liu*
Affiliation:
The Chinese University of Hong Kong, Shatin, NT, Hong Kong (e-mail: [email protected])
Yu-Zhen Janice Chen
Affiliation:
University of Massachusetts Amherst, MA01002, USA (e-mail: [email protected])
John C. S. Lui
Affiliation:
The Chinese University of Hong Kong, Shatin, NT, Hong Kong (e-mail: [email protected])
Konstantin Avrachenkov
Affiliation:
INRIA Sophia Antipolis, 06902Valbonne, France (e-mail: [email protected])
*
*Corresponding author. Email: [email protected]
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Abstract

Graphlet counting is a widely explored problem in network analysis and has been successfully applied to a variety of applications in many domains, most notatbly bioinformatics, social science, and infrastructure network studies. Efficiently computing graphlet counts remains challenging due to the combinatorial explosion, where a naive enumeration algorithm needs O(Nk) time for k-node graphlets in a network of size N. Recently, many works introduced carefully designed combinatorial and sampling methods with encouraging results. However, the existing methods ignore the fact that graphlet counts and the graph structural information are correlated. They always consider a graph as a new input and repeat the tedious counting procedure on a regular basis even if it is similar or exactly isomorphic to previously studied graphs. This provides an opportunity to speed up the graphlet count estimation procedure by exploiting this correlation via learning methods. In this paper, we raise a novel graphlet count learning (GCL) problem: given a set of historical graphs with known graphlet counts, how to learn to estimate/predict graphlet count for unseen graphs coming from the same (or similar) underlying distribution. We develop a deep learning framework which contains two convolutional neural network models and a series of data preprocessing techniques to solve the GCL problem. Extensive experiments are conducted on three types of synthetic random graphs and three types of real-world graphs for all 3-, 4-, and 5-node graphlets to demonstrate the accuracy, efficiency, and generalizability of our framework. Compared with state-of-the-art exact/sampling methods, our framework shows great potential, which can offer up to two orders of magnitude speedup on synthetic graphs and achieve on par speed on real-world graphs with competitive accuracy.

Keywords

Graphlet count estimationConvolutional neural networksDeep learning on graphNetwork analysis
Type
Research Article
Information
Network Science , Volume 9 , Special Issue S1: Complex Networks 2019 , October 2021 , pp. S23 - S60
Copyright
© The Author(s), 2020. Published by Cambridge University Press

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Footnotes

Special Issue Editor: Hocine Cherifi

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