This page contains of some of the general-purpose graph and network preprocessing algorithms developed by members of the Data Mining Research Laboratory at the Ohio State University. In particular this page has links to code, presentations and papers as they relate to local sparsification (a mechanism to reduce the edge set in the graph while retaining the community structure in triangle dense graphs); symmetrizations for directed graphs (a mechanism to convert a directed graph to an undirected graph while retaining useful information regarding the directionality) and recent results on supporting such ideas for graphs with content (CODICIL) and for streaming graphs (topological sketching).
- Venu Satuluri, Srinivasan Parthasarathy, and Yiye Ruan. 2011. Local graph sparsification for scalable clustering. In Proceedings of the 2011 ACM SIGMOD International Conference on Management of data (SIGMOD '11). PDF. (source code here: filename: sparsify1.1.tar.gz)
- Venu Satuluri and Srinivasan Parthasarathy. 2011. Symmetrizations for clustering directed graphs. In Proceedings of the 14th International Conference on Extending Database Technology (EDBT/ICDT '11). PDF. (source code here: filename: symmetrizations.tar.gz)
- Yiye Ruan, David Fuhry, and Srinivasan Parthasarathy. 2013. Efficient community detection in large networks using content and links. In Proceedings of the 22nd international conference on World Wide Web (WWW '13). ACM, New York, NY, USA, 1089-1098. PDF. (source code here: filename: codicil.code.tar.gz)
- B. Bandyopadhyay, A. Chakrabarti, D. Fuhry, S. Parthasarathy, Topological Graph Sketching for Incremental and Scalable Analytics, to appear at CIKM 2016 (source code and paper available upon request).