Towards Compressing Web Graphs
DCC '01 Proceedings of the Data Compression Conference
The webgraph framework I: compression techniques
Proceedings of the 13th international conference on World Wide Web
Fast discovery of connection subgraphs
Proceedings of the tenth 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
Finding reliable subgraphs from large probabilistic graphs
Data Mining and Knowledge Discovery
Graph OLAP: Towards Online Analytical Processing on Graphs
ICDM '08 Proceedings of the 2008 Eighth IEEE International Conference on Data Mining
Parallel PathFinder Algorithms for Mining Structures from Graphs
ICDM '09 Proceedings of the 2009 Ninth IEEE International Conference on Data Mining
Mining graph patterns efficiently via randomized summaries
Proceedings of the VLDB Endowment
Compression of weighted graphs
Proceedings of the 17th ACM SIGKDD international conference on Knowledge discovery and data mining
A framework for path-oriented network simplification
IDA'10 Proceedings of the 9th international conference on Advances in Intelligent Data Analysis
Review of bisonet abstraction techniques
Bisociative Knowledge Discovery
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We give methods to compress weighted graphs (i.e., networks or BisoNets) into smaller ones. The motivation is that large networks of social, biological, or other relations can be complex to handle and visualize. Using the given methods, nodes and edges of a give graph are grouped to supernodes and superedges, respectively. The interpretation (i.e. decompression) of a compressed 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 equals the weight of the superedge. The compression problem then consists of choosing supernodes, superedges, and superedge weights so that the approximation error is minimized while the amount of compression is maximized. In this chapter, we describe this task as the 'simple weighted graph compression problem'. We also discuss 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 outline a range of algorithms to solve them, with different trade-offs 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.