Abstract
In this paper we survey methods for performing a comparative graph analysis and explain the history, foundations and differences of such techniques of the last 50 years. While surveying these methods, we introduce a novel classification scheme by distinguishing between methods for deterministic and random graphs. We believe that this scheme is useful for a better understanding of the methods, their challenges and, finally, for applying the methods efficiently in an interdisciplinary setting of data science to solve a particular problem involving comparative network analysis.
| Original language | English |
|---|---|
| Pages (from-to) | 180-197 |
| Number of pages | 18 |
| Journal | Information Sciences |
| Volume | 346-347 |
| DOIs | |
| Publication status | Published - 10 Jun 2016 |
| Publication type | A1 Journal article-refereed |
Keywords
- Biological networks
- Computational graph theory
- Graph matching
- Network comparison
- Network similarity
- Quantitative graph theory
Publication forum classification
- Publication forum level 1
ASJC Scopus subject areas
- Artificial Intelligence
- Software
- Control and Systems Engineering
- Theoretical Computer Science
- Computer Science Applications
- Information Systems and Management