The shortest path to complex networks
S.N. Dorogovtsev, J.F.F. Mendes

TL;DR
This paper provides a comprehensive overview of complex network theory, covering foundational concepts, models, properties, and phenomena, aiming to understand the structure and dynamics of real-world networks.
Contribution
It synthesizes key developments in network science, clarifies distinctions among network types, and discusses modeling approaches and empirical findings in a unified framework.
Findings
Real networks often exhibit fat-tailed degree distributions.
Small-world and scale-free properties are prevalent in real networks.
Networks show ultraresilience against random failures but vulnerability to targeted attacks.
Abstract
1. The birth of network science. 2. What are random networks? 3. Adjacency matrix. 4. Degree distribution. 5. What are simple networks? Classical random graphs. 6. Birth of the giant component. 7. Topology of the Web. 8.Uncorrelated networks. 9. What are small worlds? 10. Real networks are mesoscopic objects. 11. What are complex networks? 12. The configuration model. 13. The absence of degree--degree correlations. 14.Networks with correlated degrees.15.Clustering. 16. What are small-world networks? 17. `Small worlds' is not the same as `small-world networks'. 18. Fat-tailed degree distributions. 19.Reasons for the fat-tailed degree distributions. 20. Preferential linking. 21. Condensation of edges. 22. Cut-offs of degree distributions. 23. Reasons for correlations in networks. 24. Classical random graphs cannot be used for comparison with real networks. 25. How to measure…
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Taxonomy
TopicsComplex Network Analysis Techniques · Opinion Dynamics and Social Influence · Network Security and Intrusion Detection
