By François Fouss, Marco Saerens, Masashi Shimbo

Community information are produced instantly through daily interactions - social networks, energy grids, and hyperlinks among info units are a couple of examples. Such info catch social and monetary habit in a kind that may be analyzed utilizing strong computational instruments. This ebook is a advisor to either uncomplicated and complex ideas and algorithms for extracting beneficial info from community info. The content material is equipped round projects, grouping the algorithms had to assemble particular kinds of info and hence resolution particular different types of questions. Examples comprise similarity among nodes in a community, status or centrality of person nodes, and dense areas or groups in a community. Algorithms are derived intimately and summarized in pseudo-code. The publication is meant basically for machine scientists, engineers, statisticians and physicists, however it can also be obtainable to community scientists dependent within the social sciences.

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Additional resources for Algorithms and Models for Network Data and Link Analysis

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The first part of this book is focused on characterizing the elements of the network, whereas the second is devoted to analyzing the global structure of the network. In particular, Chapters 6–10 address the tasks of labeling nodes, clustering nodes, and finding dense regions as well as the analysis of bipartite graphs and graph embedding. Chapter 6. In Chapter 6, we introduce some techniques for assigning a class label to an unlabeled node based on knowledge of the class of some labeled nodes and the network structure.

Exploring the Graph and Finding Connected Components Exploring the graph. A graph can be explored using standard depth-first search [706, 753] in linear time, which allows us to enumerate and explore each node in turn and to produce a depth-first search tree, a structural description of the exploration process. This depth-first search tree can then serve as a basis for solving numerous useful graph-processing problems (the interested reader is invited to refer to [706] for more details). 002 23:18:38, basic definitions and notation 21 Finding connected components.

The shortest path and the commute time distances can be regarded as two extreme ways of defining dissimilarity between graph nodes; that is, the former only considers the length without addressing the connectivity, whereas the latter only considers connectivity without addressing the length. In Chapter 3, we develop families of dissimilarities that lie in between these two distances. These quantities depend on a continuous parameter (at one limit of the value of the parameter, they converge to the shortest-path distance, whereas at the other end, they converge to the commute time distance).

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