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UID:submissions.pasc-conference.org_PASC19_sess149_msa297@linklings.com
SUMMARY:A Short Introduction to Software for Local Graph Clustering
DESCRIPTION:Minisymposium\nComputer Science and Applied Mathematics, Emerg
 ing Application Domains, Climate and Weather, Life Sciences\n\nA Short Int
 roduction to Software for Local Graph Clustering\n\nFountoulakis, Gleich, 
 Mahoney\n\nGraph clustering has many important applications in computing, 
 but due to the increasing sizes of graphs, even traditionally fast cluster
 ing methods can be computationally expensive for real-world graphs of inte
 rest. Scalability problems led to the development of local graph clusterin
 g algorithms that come with a variety of theoretical guarantees. Rather th
 an return a global clustering of the entire graph, local clustering algori
 thms return a single cluster around a given seed node or set of seed nodes
 . These algorithms improve scalability because they use time and memory re
 sources that depend only on the size of the cluster returned, instead of t
 he size of the input graph. Indeed, their running time grows linearly with
  the size of the output. In addition to scalability arguments, local graph
  clustering algorithms have proven to be very useful for identifying and i
 nterpreting small-scale and meso-scale structure in large-scale graphs. In
  this presentation, we provide (i) a brief introduction to local graph clu
 stering, focusing on the advantages compared to global graph clustering an
 d community detection, (ii) a representative example of our perspective, (
 iii) a brief description of our software named Local Graph Clustering (LGC
 ) that make it easy to work with a variety of methods.
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