Current Year

1. Metzler S: Structural Building Blocks in Graph Data. Universität des Saarlandes; 2021.
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.
Export
BibTeX
@phdthesis{SaskiaDiss21, TITLE = {Structural Building Blocks in Graph Data}, AUTHOR = {Metzler, Saskia}, LANGUAGE = {eng}, SCHOOL = {Universit{\"a}t des Saarlandes}, ADDRESS = {Saarbr{\"u}cken}, YEAR = {2021}, MARGINALMARK = {$\bullet$}, DATE = {2021}, 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.}, }
Endnote
%0 Thesis %A Metzler, Saskia %Y Miettinen, Pauli %Y Weikum, Gerhard %Y Günnemann, Stephan %+ Computational Biology and Applied Algorithmics, MPI for Informatics, Max Planck Society International Max Planck Research School, MPI for Informatics, Max Planck Society Databases and Information Systems, MPI for Informatics, Max Planck Society Databases and Information Systems, MPI for Informatics, Max Planck Society External Organizations %T Structural Building Blocks in Graph Data : Characterised by Hyperbolic Communities and Uncovered by Boolean Tensor Clustering %G eng %U http://hdl.handle.net/21.11116/0000-0008-0BC1-2 %I Universität des Saarlandes %C Saarbrücken %D 2021 %P 196 p. %V phd %9 phd %X 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.
2. Scherer M, Schmidt F, Lazareva O, Walter J, Baumbach J, Schulz MH, List M: Machine Learning for Deciphering Cell Heterogeneity and Gene Regulation. Nature Computational Science 2021, 1.
Export
BibTeX
@article{Scherer2021, TITLE = {Machine Learning for Deciphering Cell Heterogeneity and Gene Regulation}, AUTHOR = {Scherer, Michael and Schmidt, Florian and Lazareva, Olga and Walter, J{\"o}rn and Baumbach, Jan and Schulz, Marcel Holger and List, Markus}, LANGUAGE = {eng}, ISSN = {2662-8457}, DOI = {10.1038/s43588-021-00038-7}, PUBLISHER = {Nature Research}, ADDRESS = {London}, YEAR = {2021}, MARGINALMARK = {$\bullet$}, JOURNAL = {Nature Computational Science}, VOLUME = {1}, }
Endnote
%0 Journal Article %A Scherer, Michael %A Schmidt, Florian %A Lazareva, Olga %A Walter, Jörn %A Baumbach, Jan %A Schulz, Marcel Holger %A List, Markus %+ Computational Biology and Applied Algorithmics, MPI for Informatics, Max Planck Society External Organizations External Organizations Computational Biology and Applied Algorithmics, MPI for Informatics, Max Planck Society External Organizations External Organizations External Organizations %T Machine Learning for Deciphering Cell Heterogeneity and Gene Regulation : %G eng %U http://hdl.handle.net/21.11116/0000-0008-2A4A-7 %R 10.1038/s43588-021-00038-7 %7 2021 %D 2021 %J Nature Computational Science %V 1 %I Nature Research %C London %@ false