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dc.contributor.authorHu, Chufeng
dc.date.accessioned2020-05-05 16:12:05 (GMT)
dc.date.available2020-05-05 16:12:05 (GMT)
dc.date.issued2020-05-05
dc.date.submitted2020-04-29
dc.identifier.urihttp://hdl.handle.net/10012/15815
dc.description.abstractLocal graph clustering methods are used to find small- and medium-scale clusters without traversing the graph. It has been shown that the combination of Approximate Personalized PageRank (APPR) algorithm and sweep method can efficiently detect a small cluster around the starting vertex. This research explores the optimization framework proposed in the work by Fountoulakis et al., where a connection between the APPR and an l1-regularized objective function is revealed. We propose a coordinate descent method for solving the l1-regularized PageRank problem. We prove that our method has running time dependent on the number of nonzero coordinates in the optimal solution. In addition, we compare 6 optimization algorithms for solving the l1-regularized PageRank problem in large graphs. We demonstrate that the proposed coordinate descent outperforms the original proximal gradient descent, and the accelerated first order algorithms have the best performance among all algorithms measured in our experiment.en
dc.language.isoenen
dc.publisherUniversity of Waterlooen
dc.relation.urihttp://snap.stanford.edu/en
dc.relation.urihttps://gitlab.com/ChesterHu/thesis_experimenten
dc.subjectoptimizationen
dc.subjectlocal graph clusteringen
dc.subjectPageRanken
dc.titleLocal Graph Clustering Using l1-regularized PageRank Algorithmsen
dc.typeMaster Thesisen
dc.pendingfalse
uws-etd.degree.departmentDavid R. Cheriton School of Computer Scienceen
uws-etd.degree.disciplineComputer Scienceen
uws-etd.degree.grantorUniversity of Waterlooen
uws-etd.degreeMaster of Scienceen
uws.contributor.advisorLi, Yuying
uws.contributor.advisorFountoulakis, Kimon
uws.contributor.affiliation1Faculty of Mathematicsen
uws.published.cityWaterlooen
uws.published.countryCanadaen
uws.published.provinceOntarioen
uws.typeOfResourceTexten
uws.peerReviewStatusUnrevieweden
uws.scholarLevelGraduateen


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