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  Structural Building Blocks in Graph Data

Metzler, S. (2021). Structural Building Blocks in Graph Data. PhD Thesis, Universität des Saarlandes, Saarbrücken.

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Genre: Thesis
Subtitle : Characterised by Hyperbolic Communities and Uncovered by Boolean Tensor Clustering

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 Creators:
Metzler, Saskia1, 2, Author           
Miettinen, Pauli3, Advisor           
Weikum, Gerhard3, Advisor           
Günnemann, Stephan4, Advisor
Affiliations:
1Computational Biology and Applied Algorithmics, MPI for Informatics, Max Planck Society, ou_40046              
2International Max Planck Research School, MPI for Informatics, Max Planck Society, Campus E1 4, 66123 Saarbrücken, DE, ou_1116551              
3Databases and Information Systems, MPI for Informatics, Max Planck Society, ou_24018              
4External Organizations, ou_persistent22              

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 Abstract: Graph data nowadays easily become so large that it is infeasible to study the underlying structures manually. Thus, computational methods are needed to uncover large-scale structural information. In this thesis, we present methods to understand and summarise large networks.
We propose the hyperbolic community model to describe groups of more densely connected nodes within networks using very intuitive parameters. The model accounts for a
frequent connectivity pattern in real data: a few community members are highly interconnected; most members mainly have ties to this core. Our model fits real data much better than previously-proposed models. Our corresponding random graph generator, HyGen, creates graphs with realistic intra-community structure.
Using the hyperbolic model, we conduct a large-scale study of the temporal evolution of communities on online question–answer sites. We observe that the user activity within a community is constant with respect to its size throughout its lifetime, and a small group of users is responsible for the majority of the social interactions.
We propose an approach for Boolean tensor clustering. This special tensor factorisation is restricted to binary data and assumes that one of the tensor directions has only non-overlapping factors. These assumptions – valid for many real-world data, in particular time-evolving networks – enable the use of bitwise operators and lift much of the computational complexity from the task.

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Language(s): eng - English
 Dates: 2021-02-2420212021
 Publication Status: Published in print
 Pages: 196 p.
 Publishing info: Saarbrücken : Universität des Saarlandes
 Table of Contents: -
 Rev. Type: -
 Identifiers: BibTex Citekey: SaskiaDiss21
 Degree: PhD

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