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DTSTART;TZID=Europe/Stockholm:20190614T133000
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UID:submissions.pasc-conference.org_PASC19_sess149@linklings.com
SUMMARY:MS51 - Identifying Relevant Communities in Immense Networks: Clust
 ering Algorithms that Leverage High-Performance Computing
DESCRIPTION:Minisymposium\nComputer Science and Applied Mathematics, Emerg
 ing Application Domains, Climate and Weather, Life Sciences\n\nSubtle Char
 acteristics of Clustering Algorithms\n\nClimer\n\nEven though they are fre
 quently arbitrarily employed, clustering algorithms are widely diverse and
  carry underlying characteristics that may dramatically impact results. Wh
 en multiple methods are employed, the results can vary radically and it fr
 equently is difficult to objectively measure the most ...\n\n-------------
 --------\nClustering Algorithms Open Discussion\n\nClimer, Fountoulakis, H
 amann, Jacobson\n\nRound table discussion with Sharlee Climer, Kimon Fount
 oulakis, Michael Hamann, and Dan Jacobson.\n\n---------------------\nA Sho
 rt Introduction to Software for Local Graph Clustering\n\nFountoulakis, Gl
 eich, Mahoney\n\nGraph clustering has many important applications in compu
 ting, but due to the increasing sizes of graphs, even traditionally fast c
 lustering methods can be computationally expensive for real-world graphs o
 f interest. Scalability problems led to the development of local graph clu
 stering algorithms th...\n\n---------------------\nDistributed Graph Clust
 ering Using Modularity and Map Equation\n\nHamann, Strasser, Wagner, Zeitz
 \n\nWe study large-scale, distributed graph clustering. Given an undirecte
 d graph, our objective is to partition the nodes into disjoint sets called
  clusters. A cluster should contain many internal edges while being sparse
 ly connected to other clusters. In the context of a social network, a clus
 ter coul...\n
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