Representing Graph Metrics with Fewest Edges
STACS '03 Proceedings of the 20th Annual Symposium on Theoretical Aspects of Computer Science
The Link Database: Fast Access to Graphs of the Web
DCC '02 Proceedings of the Data Compression Conference
Recommender Systems Research: A Connection-Centric Survey
Journal of Intelligent Information Systems
Structure and evolution of online social networks
Proceedings of the 12th ACM SIGKDD international conference on Knowledge discovery and data mining
Graph summarization with bounded error
Proceedings of the 2008 ACM SIGMOD international conference on Management of data
Efficient aggregation for graph summarization
Proceedings of the 2008 ACM SIGMOD international conference on Management of data
Mining graph patterns efficiently via randomized summaries
Proceedings of the VLDB Endowment
Network Simplification with Minimal Loss of Connectivity
ICDM '10 Proceedings of the 2010 IEEE International Conference on Data Mining
Compression of weighted graphs
Proceedings of the 17th ACM SIGKDD international conference on Knowledge discovery and data mining
Distance Preserving Graph Simplification
ICDM '11 Proceedings of the 2011 IEEE 11th International Conference on Data Mining
Survey: A survey of the algorithmic aspects of modular decomposition
Computer Science Review
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Nowadays, massive data sets of graph-like data arise in various application domains ranging from bioinformatics to social networks and communication networks analysis. The abundance of such kind of data calls for innovative techniques for storing, managing and processing graph-like data. In order to fulfill these requirements, in this paper we propose: (i) a model for representing compressed weighted graphs, and (ii) an efficient and effective compression algorithm which, leveraging on modular decomposition theory, is capable of exploiting structural properties of graphs in order to obtain highly compact and accurate compressed representations. Such compressed graphs can be used in place of the original graphs in order to enhance the performance of graph clustering algorithms in all contexts where a little inaccuracy in the results is acceptable in order to gain computational efficiency. The paper is completed by an experimental study which shows the effectiveness of the proposed approach in the context of graph clustering.