Network Analysis: Overview

Heterogeneous information networks or BisoNets, as they are called in the context of bisociative knowledge discovery, are a flexible and popular form of representing data in numerous fields. Additionally, such networks can be created or derived from other types of information using, e.g., the methods given in Part II of this volume. This part of the book describes various network algorithms for the exploration and analysis of BisoNets. Their general goal is to support and partially even automate the process of bisociation. More specific goals are to allow navigation of BisoNets by indirect and predicted relationships and by analogy, to produce explanations for discovered relationships, and to help abstract and summarise BisoNets for more effective visualisation.

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[4]  Fang Zhou,et al.  Review of BisoNet Abstraction Techniques , 2012, Bisociative Knowledge Discovery.

[5]  Michael R. Berthold,et al.  Node Similarities from Spreading Activation , 2010, 2010 IEEE International Conference on Data Mining.

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[9]  Tobias Kötter,et al.  Towards Discovery of Subgraph Bisociations , 2012, Bisociative Knowledge Discovery.

[10]  Luc De Raedt,et al.  BiQL: A Query Language for Analyzing Information Networks , 2012, Bisociative Knowledge Discovery.