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LOCATION:HG D 1.2
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UID:submissions.pasc-conference.org_PASC24_sess124_msa281@linklings.com
SUMMARY:Parallel Algorithms for Dynamic Graph Clustering
DESCRIPTION:Minisymposium\n\nJohannes Langguth (Simula Research Laboratory
 , University of Bergen)\n\nWe consider the problem of incremental graph cl
 ustering where the graph to be clustered is given as a sequence of disjoin
 t subsets of the edge set. The problem appears when dealing with graphs th
 at are created over time, such as online social networks where new users a
 ppear continuously, or protein interaction networks when new proteins are 
 discovered. For very large graphs, it is computationally too expensive to 
 repeatedly apply standard clustering algorithms.\nInstead, algorithms whos
 e time complexity only depends on the size of the incoming subset of edges
  in every step are needed. At the same time, such algorithms should find c
 lusterings whose quality is close to that produced by offline algorithms. 
 We discuss the computational model and present an incremental clustering a
 lgorithm, along with its parallel implementation. The scalability results 
 suggest that our method is well suited for clustering massive graphs with 
 acceptable running times while retaining a large fraction of the clusterin
 g quality.\n\nDomain: Computational Methods and Applied Mathematics\n\nSes
 sion Chairs: Dimosthenis Pasadakis (Università della Svizzera italiana) an
 d Olaf Schenk (Università della Svizzera italiana, ETH Zurich)
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