Network analysis: overview
Hannu Toivonen
- 01 Jan 2012
- pp 144-146
TL;DR: This part of the book describes various network algorithms for the exploration and analysis of BisoNets to support and partially even automate the process of bisociation.
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Abstract: 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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Citations
Towards bisociative knowledge discovery
Michael R. Berthold
- 01 Jan 2012
TL;DR: This article outlines a framework for the discovery of new connections between domains (so called bisociations), supporting the creative discovery process in a more powerful way and motivate this approach, shows the difference to classical data analysis and concludes by describing a number of different types of domain-crossing connections.
References
Bisociative knowledge discovery
Michael R. Berthold
- 29 Oct 2011
TL;DR: This article focuses on the discovery of new connections between domains (so called bisociations), supporting the creative discovery process in a novel way and motivating this approach, shows the difference to classical data analysis and concludes by briefly illustrating some types of domain-crossing connections.
A query language for analyzing networks
Anton Dries,Siegfried Nijssen,Luc De Raedt +2 more
- 02 Nov 2009
TL;DR: A data model and a query language for facilitating the analysis of networks that provides for a closure property, in which the output of every query can be stored in the database and used for further querying.
Simplification of networks by edge pruning
Fang Zhou,Sébastien Mahler,Hannu Toivonen +2 more
- 01 Jan 2012
TL;DR: A rough semantic analysis of the removed edges indicates that few important edges were removed, and that the proposed approach could be a valuable tool in aiding users to view or explore weighted graphs.
•Book
Node similarities from spreading activation
Kilian Thiel,Michael R. Berthold +1 more
- 01 Jan 2012
TL;DR: Two methods to derive two different kinds of node similarities in a network based on their neighborhood are proposed, using standard node measures but derived from spreading activation patterns over time.
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Missing) concept discovery in heterogeneous information networks
Tobias Kötter,Michael R. Berthold +1 more
- 01 Jan 2012
TL;DR: A new approach to extract existing (or detect missing) concepts from a loosely integrated collection of information units by means of concept graph detection and defines a concept by a quasi bipartite sub-graph of a bigger network.