By Mohammed Zuhair Al-Taie, Seifedine Kadry

This examine monograph presents the potential to profit the speculation and perform of graph and community research utilizing the Python programming language. The social community research options, incorporated, can help readers to successfully examine social info from Twitter, fb, LiveJournal, GitHub and so forth at 3 degrees of intensity: ego, team, and neighborhood. they are going to be capable of examine militant and innovative networks and candidate networks in the course of elections. for example, they are going to find out how the Ebola virus unfold via groups.

Practically, the booklet is acceptable for classes on social community research in all disciplines that use social method. within the learn of social networks, social community research makes an enticing interdisciplinary learn sector, the place computing device scientists and sociologists deliver their competence to a degree that would permit them to fulfill the demanding situations of this fast-developing box. desktop scientists have the information to parse and technique facts whereas sociologists have the event that's required for effective facts modifying and interpretation. Social community research has effectively been utilized in numerous fields comparable to overall healthiness, cyber safety, company, animal social networks, info retrieval, and communications.

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Extra resources for Python for Graph and Network Analysis

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5 NetworkX NetworkX is a Python language software package and an open-source tool for the creation, manipulation, and study of the structure, dynamics, and functions of complex networks. It is a computational network modeling tool and not a software tool development. It can load, store, and analyze networks, generate new networks, build network models, and draw networks. The first public release of the library, which is all based on Python, was in April 2005, and the library can engage with languages other than Python such as C, C++, and FORTRAN.

On social networks, hierarchical clustering means that we partition network nodes (actors) into groups of similar nodes using some distance measure. Two implementations were developed for the hierarchical clustering algorithm: agglomerative and divisive. The agglomerative version starts with clustering all data items into clusters (or groups) based on a distance metric. The closest pair of clusters is then merged by moving up the hierarchy into a single cluster, while the correlation between data items of different clusters decreases as we move from the bottom (leaves) to the top (root).

For example, in the Internet, nodes refer to routers and links refer to optical fibers. In WWW, nodes represent document files and links represent hyperlinks. In the scientific citation network, nodes represent papers and links represent citations. In a social network, node individuals and links represent relations. Example generators of complex networks include Erdos-Renyi graph, Watts-Strogatz graph, Barabasi-Albert graph, and Random-lobster graph. 3 Properties of Networks Networks are characterized by two types of properties: static and dynamic.

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