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Generating Scale-Free Networks with Graph Theoretic Properties

Generating Scale-Free Networks with Graph Theoretic Properties

Speaker: Md. Saidur Rahman – Dhaka, Bangladesh
Topic(s): Computational Theory, Algorithms and Mathematics , Information Systems, Search, Information Retrieval, Database Systems, Data Mining, Data Science

Abstract

Generating synthetic networks with special properties is an important topic of research in network science. Generating algorithms enable researchers to replicate complex network structures, facilitating in-depth analysis and validate different algorithms. One such important type of network is a scale-free network. Many real networks, irrespective of their discipline, function or scope follow the scale free property. In a scale-free network the vertex connectivity follows a power-law distribution where the probability of a vertex having degree k is proportional to k−γ with 2 < γ < 3. Before the discovery of scale-free networks, synthetic networks were generated using the Erd?s–Rényi random network model. While this model is simple to understand and implement, it is unable to capture certain real-world properties, such as the presence of high-degree hub vertices. To address this, Barabási and Albert introduced a preferential attachment model that grows over time and exhibits a power-law degree distribution. Starting from Albert- Barabási model, in recent years several models have been developed which can generate scale-free networks. These models focus on generating scale-free deterministic, pseudo-fractal, hierarchical etc. various types of networks. But generating scale-free networks of desired graph theoretic properties has been a key scope of work. We want to generate networks that shows both the scale-free properties and some graph theoretic properties. In this talk, we focus on some recent algorithms for generation scale-free networks with desired graph theoretic properties. We also discuss some open problems and future direction of research.

About this Lecture

Number of Slides: 70
Duration: 45 minutes
Languages Available: English
Last Updated:

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