Local Community Detection in Dynamic Graphs Using Personalized Centrality. Nathan, E., Zakrzewska, A., Riedy, J., & Bader, D. A. Algorithms, August, 2017.
doi  abstract   bibtex   
Analyzing massive graphs poses challenges due to the vast amount of data available. Extracting smaller relevant subgraphs allows for further visualization and analysis that would otherwise be too computationally intensive. Furthermore, many real data sets are constantly changing, and require algorithms to update as the graph evolves. This work addresses the topic of local community detection, or seed set expansion, using personalized centrality measures, specifically PageRank and Katz centrality. We present a method to efficiently update local communities in dynamic graphs. By updating the personalized ranking vectors, we can incrementally update the corresponding local community. Applying our methods on real-world graphs, we are able to obtain speedups of up to 60$\times$ compared to static recomputation while maintaining an average recall of 0.94 of the highly ranked vertices returned. Next, we investigate how approximations of a centrality vector affect the resulting local community. Specifically, our method that guarantees that the vertices returned in the community are the highly ranked vertices from a personalized centrality metric.
@article{localcomm-2017,
  author = {Eisha Nathan and Anita Zakrzewska and Jason Riedy
                  and David A. Bader},
  title = {Local Community Detection in Dynamic Graphs Using
                  Personalized Centrality},
  journal = {Algorithms},
  year = 2017,
  month = aug,
  abstract = {Analyzing massive graphs poses challenges due to the
                  vast amount of data available.  Extracting smaller
                  relevant subgraphs allows for further visualization
                  and analysis that would otherwise be too
                  computationally intensive.  Furthermore, many real
                  data sets are constantly changing, and require
                  algorithms to update as the graph evolves.  This
                  work addresses the topic of local community
                  detection, or seed set expansion, using personalized
                  centrality measures, specifically PageRank and Katz
                  centrality.  We present a method to efficiently
                  update local communities in dynamic graphs. By
                  updating the personalized ranking vectors, we can
                  incrementally update the corresponding local
                  community.  Applying our methods on real-world
                  graphs, we are able to obtain speedups of up to
                  60$\times$ compared to static recomputation while
                  maintaining an average recall of 0.94 of the highly
                  ranked vertices returned.  Next, we investigate how
                  approximations of a centrality vector affect the
                  resulting local community. Specifically, our method
                  that guarantees that the vertices returned in the
                  community are the highly ranked vertices from a
                  personalized centrality metric.},
  issn = {1999-4893},
  doi = {10.3390/a10030102},
  volume = 10,
  number = 3,
  article_number = {102},
  projtag = {grateful, hpda}
}
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