Generating Data for Testing Community Detection Algorithms
These days Internet usage has increased. People of all age groups use Internet and this has led to a new research field called complex networks. Complex networks such as social networks, biological networks, technological networks, etc., have become the i
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Abstract These days Internet usage has increased. People of all age groups use Internet and this has led to a new research field called complex networks. Complex networks such as social networks, biological networks, technological networks, etc., have become the interest of many researchers because of their wide range of applications. These complex networks have many properties like scale-free networks, transitivity, presence of community structure in these networks. Community detection is one of the most active fields in complex networks because it has many practical applications. In this paper we have studied about community detection. We have also discussed about the techniques of generating data for comparing various community detection algorithms. Keywords Community
GN benchmark LFR benchmark
1 Introduction Nowadays real systems have grown in size tremendously. They contain millions of actors and have different relationships. Complex networks are the powerful modeling tools which represent most real-world systems. Complex network paradigm is one of the modeling tools which have spread through several application fields such as sociology, communication, computer science, biology, and physics, and so on during last decades. Complex networks can be represented in the form of large graphs which have large number of nodes and different types of relationships with nontrivial properties. These nodes can be anything: a person, an organization, a computer, or a biological cell. Nodes can have different sizes or attributes which M.S. Ahuja (&) Guru Nanak Dev University, Regional Campus, Gurdaspur, India e-mail: [email protected] Jatinder Singh KC Group of Institutes, Nawashahr, India e-mail: [email protected] © Springer Science+Business Media Singapore 2016 S.C. Satapathy et al. (eds.), Proceedings of the International Congress on Information and Communication Technology, Advances in Intelligent Systems and Computing 438, DOI 10.1007/978-981-10-0767-5_43
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represent a property of real system objects. These graphs can be directed, undirected, or weighted. A complex network has its roots in graph theory. Few examples of complex networks are Internet maps (IP, Routers [1], web graphs (hyperlinks between pages) [2], data exchange (emails) [3], social networks (Facebook, Twitter, scientist collaboration networks), biological networks (protein interaction, epidemic networks), etc. Complex networks have nontrivial properties so they cannot be explained by uniform random, regular, or complete models [4]. This has resulted in definition of set of statistics which have become fundamental properties of complex networks. These properties are now being used by many researchers for studying various phenomena’s like spreading of information [5], protocol performance, etc. But a major challenge in the study of complex networks is how to collect data for analysis. We cannot directly collect data from these real world complex networks to study them. So researches have to make an a
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