By Pierluigi Contucci, Ronaldo Menezes, Andrea Omicini, Julia Poncela-Casasnovas
A community is a mathematical item including a suite of issues which are attached to one another in a few model via strains. It seems this easy description corresponds to a bewildering array of structures within the actual global, starting from technological ones equivalent to the web and world-wide-web, organic networks equivalent to that of connections of the anxious structures, nutrition webs or protein interactions, infrastructural platforms equivalent to networks of roads, airports or the power-grid, to styles of social relationships reminiscent of friendship, intercourse companions, community of Hollywood actors, co-authorship networks and lots of more.
Recent years have witnessed a great deal of curiosity in the medical neighborhood within the homes of those networks. The emergence of the net specifically, coupled with the frequent availability of cheap computing assets has facilitated reports starting from huge scale empirical research of networks within the actual international, to the improvement of theoretical types and instruments to discover many of the homes of those platforms. The examine of networks is largely interdisciplinary and crucial advancements have happened in lots of fields, together with arithmetic, physics, computing device and knowledge sciences, biology and the social sciences.
This ebook brings jointly a set of state-of-the-art examine within the box from a various array of researchers starting from physicists to social scientists and offers them in a coherent type, highlighting the powerful interconnections among different components. subject matters incorporated are social networks and social media, opinion and innovation diffusion, organic and health-related networks, language networks, in addition to community idea, group detection, or progress types for advanced Networks.
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Additional resources for Complex Networks V: Proceedings of the 5th Workshop on Complex Networks CompleNet 2014
In: Proceedings of ACM IMC, pp. 29–42 (2007) 22. : Networks: an introduction. Oxford University Press (2009) 23. : Scientific collaboration networks. ii. shortest paths, weighted networks, and centrality. Phys. Rev. E 64, 016,132 (2001) 24. : Modularity and community structure in networks. PNAS 103(23), 8577–8582 (2006) 25. : Quantifying social group evolution. Nature 446 (2007) 26. : Biological network comparison using graphlet degree distribution. Bioinformatics 23(2), e177–e183 (2007) 27. : Graphlet-based edge clustering reveals pathogen-interacting proteins.
For longer periods such as Δ t=1 month, for most network types, the LCC sizes do not decrease significantly when increasing w from 1 to 3, but the LCC sizes do decrease drastically when increasing w from 3 to 4. Therefore, w less than 4 should be used. Of the remaining ws, we henceforth focus only on w=3, because the stronger the value of w, the stronger the communication, and thus, we favor w=3 over w=1 or w=2. Systematic Dynamic and Heterogeneous Analysis 31 In summary, we continue by studying the following combinations of parameter choices: 1) Δ t=1 week and w=1, and 2) Δ t=1 month and w = 3.
However, whereas these studies have demonstrated the potential of using diverse information encoded in multiple communication types, the studies have typically been domain-specific or have not reasoned about the mechanisms underlying the observations [3, 30]. Further, many of the current social network studies (including those listed earlier) have analyzed only the static representation of a social network that is in fact dynamic in nature [21, 24], thus losing valuable temporal information. More recent studies have recognized the potential of accounting for the dynamic nature of the network data [2, 11, 15, 14, 25].