By Mrutyunjaya Panda, Satchidananda Dehuri, Gi-Nam Wang
With the proliferation of social media and online groups in networked international a wide gamut of knowledge has been amassed and saved in databases. the speed at which such info is saved is turning out to be at a gorgeous cost and pushing the classical equipment of knowledge research to their limits. This e-book provides an built-in framework of modern empirical and theoretical examine on social community research according to quite a lot of concepts from a number of disciplines like information mining, social sciences, arithmetic, records, physics, community technological know-how, laptop studying with visualization recommendations and safety. The e-book illustrates the potential for multi-disciplinary suggestions in quite a few actual existence difficulties and intends to inspire researchers in social community research to layout more advantageous instruments by way of integrating swarm intelligence and knowledge mining.
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Additional resources for Social Networking: Mining, Visualization, and Security
203–214 (2010) 67. : Rumors in a network: Who’s the culprit? IEEE Transactions on Information Theory 57(8), 5163–5181 (2011) 68. : Rumor centrality: a universal source detector. In: Proceedings of the 12th ACM SIGMETRICS/PERFORMANCE Joint International Conference on Measurement and Modeling of Computer Systems, SIGMETRICS 2012, pp. 199–210 (2012) 69. : Rooting out the rumor culprit from suspects. SI] (2013) 70. : Information source detection in the sir model: A sample path based approach. In: Proc.
1483–1489 (2012) 73. : Spotting culprits in epidemics: How many and which ones? In: 2012 IEEE 12th International Conference on Data Mining (ICDM), pp. 11–20 (2012) 74. : The Minimum Description Length Principle (Adaptive Computation and Machine Learning). The MIT Press (2007) Structure and Evolution of Online Social Networks Saptarshi Ghosh and Niloy Ganguly Abstract. Social networks are complex systems which evolve through interactions among a growing set of actors or users. A popular methodology of studying such systems is to use tools of complex network theory to analyze the evolution of the networks, and the topological properties that emerge through the process of evolution.
Betweeness refers to the number of times that a node lies on the shortest paths between any pair of nodes in the network graph. 5. Closeness refers to the inverse of the sum of the shortest distances between each node and every other node in the network graph. 2 Individuals that sign up to receive the latest deals and brochures from the company. 10 A. P. 6. Page-Rank was introduced by Google to quantify the importance of a particular web-page . Given web-pages as nodes and hyperlinks as edges, the Page-Rank for a particular web-page can be quantified as follows PR(pi ) = ∑ p j ∈ψ pi PR(p j ) L(p j ) (11) where PR(pi ) is the Page-Rank value for page pi , ψ pi is the set of web-pages link to pi , and L(p j ) is the number of outbound links on p j .