@inproceedings{54d6a4f5d93048c1b2ab5b7550a3acf6,
title = "Fast incremental community detection on dynamic graphs",
abstract = "Community detection, or graph clustering, is the problem of finding dense groups in a graph. This is important for a variety of applications, from social network analysis to biological interactions. While most work in community detection has focused on static graphs, real data is usually dynamic, changing over time. We present a new algorithm for dynamic community detection that incrementally updates clusters when the graph changes. The method is based on a greedy, modularity maximizing static approach and stores the history of merges in order to backtrack. On synthetic graph tests with known ground truth clusters, it can detect a variety of structural community changes for both small and large batches of edge updates.",
keywords = "Community detection, Dynamic graphs, Graph clustering, Graphs",
author = "Anita Zakrzewska and Bader, {David A.}",
note = "Publisher Copyright: {\textcopyright} Springer International Publishing Switzerland 2016.; 11th International Conference on Parallel Processing and Applied Mathematics, PPAM 2015 ; Conference date: 06-09-2015 Through 09-09-2015",
year = "2016",
doi = "10.1007/978-3-319-32149-3_20",
language = "English (US)",
isbn = "9783319321486",
series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",
publisher = "Springer Verlag",
pages = "207--217",
editor = "Ewa Deelman and Jack Dongarra and Konrad Karczewski and Roman Wyrzykowski and Jacek Kitowski and Kazimierz Wiatr",
booktitle = "Parallel Processing and Applied Mathematics - 11th International Conference, PPAM 2015, Revised Selected Papers",
address = "Germany",
}