Computational Network Science: An Algorithmic Approach (Computer Science Reviews and Trends)
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The emerging field of network science represents a new style of research that can unify such traditionally-diverse fields as sociology, economics, physics, biology, and computer science. It is a powerful tool in analyzing both natural and man-made systems, using the relationships between players within these networks and between the networks themselves to gain insight into the nature of each field. Until now, studies in network science have been focused on particular relationships that require varied and sometimes-incompatible datasets, which has kept it from being a truly universal discipline.
Computational Network Science seeks to unify the methods used to analyze these diverse fields. This book provides an introduction to the field of Network Science and provides the groundwork for a computational, algorithm-based approach to network and system analysis in a new and important way. This new approach would remove the need for tedious human-based analysis of different datasets and help researchers spend more time on the qualitative aspects of network science research.
- Demystifies media hype regarding Network Science and serves as a fast-paced introduction to state-of-the-art concepts and systems related to network science
- Comprehensive coverage of Network Science algorithms, methodologies, and common problems
- Includes references to formative and updated developments in the field
- Coverage spans mathematical sociology, economics, political science, and biological networks
Hence, the cascade model of network diffusion is shown to stop and leave out certain parts of the network and this could be used as a model for community detection. 58 Computational Network Science: An Algorithmic Approach Fig. 6.15. Student A influenced other students C, E, H, and I in registering the network course. Fig. 6.16. Detected students community who registered for the networking course. Diffusion and Contagion 59 6.4.4 Independent Contagion Model In a related independent.
Ideas among people (i.e., following). Twitter can be used by small or large groups to form crowd sourcing. For example, in the small network, Ubiquity of Networks 5 when a family stays organized about their travel itinerary, there are disparate opinions. In the large network, a large social project, such as a protest, can be planned. Twitter can be used to work semi-anonymously with others. Twitter’s hashtag (i.e., #) is a feature for labeling a topic. Anyone may introduce or reuse a.
The other hand, some resource owners may wish to make their resources available far beyond their immediate surroundings. It would be useful if the resource owner could have an interface agent in order to expose availability of the resource to remote parts of the NO. This is discussed in the following section. 10.1.1 Intermediaries Some agents may play intermediary roles to facilitate resource access among other agents. The simplest intermediary role is when a resource owner agent delegates.
Networked Science. Princeton University Press. Penrose, M., 2003. Random Geometric Graphs. Oxford University Press. Reingold, H., 2000. The Virtual Community. MIT Press. Seung, S., 2012. Connectome: How Brain’s Wiring Makes Us Who We Are. Mariner Books. Watts, D., Strogatz, S., 1998. Collective dynamic of small-world networks. Nature 393 (6684), 440–442. 14 Computational Network Science: An Algorithmic Approach EXERCISES 1. Using examples, describe how animal swarms are networked. 2.
Is disconnected. Let us consider a cluster that is a subset of nodes s and each node may count the ratio r as node. r is the density of its neighbors in s versus the total number of its neighbors. In the set s, the node with the minimum r value rmin yields the value called density of cluster (used in Chapter 7). Whereas centrality is a microlevel measure, centralization is a macrolevel measure, which measures variance in the distribution of centrality in a network. We show the most generic form.