Extending the Applicability of Graphlets to Directed Networks

• Type: Article in International Scientific Journal
• Date: 2017

Authors

Aparicio, D (Author) Other; Pedro Ribeiro (Author) FCUP; Silva, F (Author) FCUP

Journal

Title: IEEE/ACM Transactions on Computational Biology and Bioinformatics

Vol. 14

Pages: 1302-1315

ISSN: 1545-5963

Publisher: IEEE

Indexing

ISI Web of Knowledge - 12 Citations

Scopus - 11 Citations

Other information

Authenticus ID: P-00N-536

DOI: 10.1109/tcbb.2016.2586046

Abstract (EN): With recent advances in high-throughput cell biology, the amount of cellular biological data has grown drastically. Such data is often modeled as graphs (also called networks) and studying them can lead to new insights intomolecule-level organization. A possible way to understand their structure is by analyzing the smaller components that constitute them, namely network motifs and graphlets. Graphlets are particularly well suited to compare networks and to assess their level of similarity due to the rich topological information that they offer but are almost always used as small undirected graphs of up to five nodes, thus limiting their applicability in directed networks. However, a large set of interesting biological networks such asmetabolic, cell signaling, or transcriptional regulatory networks are intrinsically directional, and using metrics that ignore edge direction may gravely hinder information extraction. Our main purpose in this work is to extend the applicability of graphlets to directed networks by considering their edge direction, thus providing a powerful basis for the analysis of directed biological networks. We tested our approach on two network sets, one composed of synthetic graphs and another of real directed biological networks, and verified that they were more accurately grouped using directed graphlets than undirected graphlets. It is also evident that directed graphlets offer substantially more topological information than simple graph metrics such as degree distribution or reciprocity. However, enumerating graphlets in large networks is a computationally demanding task. Our implementation addresses this concern by using a state-of-the-art data structure, the g-trie, which is able to greatly reduce the necessary computation. We compared our tool to other state-of-the art methods and verified that it is the fastest general tool for graphlet counting.

Language: English

Type (Professor's evaluation): Scientific

No. of pages: 14