Towards Compressing Web Graphs
DCC '01 Proceedings of the Data Compression Conference
The webgraph framework I: compression techniques
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Fast discovery of connection subgraphs
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Efficient aggregation for graph summarization
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ICDM '08 Proceedings of the 2008 Eighth IEEE International Conference on Data Mining
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ICDM '09 Proceedings of the 2009 Ninth IEEE International Conference on Data Mining
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IDA'10 Proceedings of the 9th international conference on Advances in Intelligent Data Analysis
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Bisociative Knowledge Discovery
Network compression by node and edge mergers
Bisociative Knowledge Discovery
On compressing weighted time-evolving graphs
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Self-Organizing map and tree topology for graph summarization
ICANN'12 Proceedings of the 22nd international conference on Artificial Neural Networks and Machine Learning - Volume Part II
Privacy Preservation by k-Anonymization of Weighted Social Networks
ASONAM '12 Proceedings of the 2012 International Conference on Advances in Social Networks Analysis and Mining (ASONAM 2012)
Speeding up graph clustering via modular decomposition based compression
Proceedings of the 28th Annual ACM Symposium on Applied Computing
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We propose to compress weighted graphs (networks), motivated by the observation that large networks of social, biological, or other relations can be complex to handle and visualize. In the process also known as graph simplification, nodes and (unweighted) edges are grouped to supernodes and superedges, respectively, to obtain a smaller graph. We propose models and algorithms for weighted graphs. The interpretation (i.e. decompression) of a compressed, weighted graph is that a pair of original nodes is connected by an edge if their supernodes are connected by one, and that the weight of an edge is approximated to be the weight of the superedge. The compression problem now consists of choosing supernodes, superedges, and superedge weights so that the approximation error is minimized while the amount of compression is maximized. In this paper, we formulate this task as the 'simple weighted graph compression problem'. We then propose a much wider class of tasks under the name of 'generalized weighted graph compression problem'. The generalized task extends the optimization to preserve longer-range connectivities between nodes, not just individual edge weights. We study the properties of these problems and propose a range of algorithms to solve them, with different balances between complexity and quality of the result. We evaluate the problems and algorithms experimentally on real networks. The results indicate that weighted graphs can be compressed efficiently with relatively little compression error.