![]() ![]() Nettleton, D.F.: Data mining of social networks represented as graphs. ![]() The system is made publicly available in a Github Java project. The results show that a close fit can be achieved between the initial user specification and the generated data, and that the algorithms have potential for scale up. The methods used are the R-MAT and Louvain algorithms, with some modifications, for graph generation and community labeling respectively, and the development of a Java based system for the data generation using an original seed assignment algorithm followed by a second algorithm for weighted and probabilistic data propagation to neighbors and other nodes. The main aim in this work is to implement an easy to use standalone end-user application which addresses the aforementioned issues. The main focus is the generation of realistic data, its assignment to and propagation within the graph. The issues to address are first to obtain a graph with a social network type structure, label it with communities. S2CID 14292535.The motivation of the work in this paper is due to the need in research and applied fields for synthetic social network data due to (i) difficulties to obtain real data and (ii) data privacy issues of the real data. "Scale-free networks as preasymptotic regimes of superlinear preferential attachment". ![]() ^ a b Krapivsky, Paul Krioukov, Dmitri (21 August 2008)."Rare and everywhere: Perspectives on scale-free networks". "Power-law distributions in empirical data". ^ a b c Clauset, Aaron Cosma Rohilla Shalizi M."Scale-Free Graph with Preferential Attachment and Evolving Internal Vertex Structure". ![]() Proceedings of the National Academy of Sciences. "Structure and tie strengths in mobile communication networks". Bianconi–Barabási model – model in network science Pages displaying wikidata descriptions as a fallback.Barabási–Albert model – algorithm for generating random networks Pages displaying wikidata descriptions as a fallback.Webgraph – Graph of connected web pages.Complex network – Network with non-trivial topological features.Scale invariance – Features that do not change if length or energy scales are multiplied by a common factor.Bose–Einstein condensation (network theory) – model in network science Pages displaying wikidata descriptions as a fallback.Erdős–Rényi model – Two closely related models for generating random graphs.Random graph – Graph generated by a random process.Theoretically, maximum likelihood estimation with random friends lead to a smaller bias and a smaller variance compared to classical approach based on uniform sampling. It has been recently proposed to sample random friends (i.e., random ends of random links) who are more likely come from the tail of the degree distribution as a result of the friendship paradox. However, since uniform sampling does not obtain enough samples from the important heavy-tail of the power law degree distribution, this method can yield a large bias and a variance. P ( k ) ∼ k − γ of a scale-free network is typically done by using the maximum likelihood estimation with the degrees of a few uniformly sampled nodes. That is, the fraction P( k) of nodes in the network having k connections to other nodes goes for large values of k as A scale-free network is a network whose degree distribution follows a power law, at least asymptotically. ![]()
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