The DOI matches its Crossref record; 22 of the 22 references it cites were found in a registry or an index.
Graph summarization with bounded error
Abstract
We propose a highly compact two-part representation of a given graph G consisting of a graph summary and a set of corrections. The graph summary is an aggregate graph in which each node corresponds to a set of nodes in G, and each edge represents the edges between all pair of nodes in the two sets. On the other hand, the corrections portion specifies the list of edge-corrections that should be applied to the summary to recreate G. Our representations allow for both lossless and lossy graph compression with bounds on the introduced error. Further, in combination with the MDL principle, they yield highly intuitive coarse-level summaries of the input graph G. We develop algorithms to construct highly compressed graph representations with small sizes and guaranteed accuracy, and validate our approach through an extensive set of experiments with multiple real-life graph data sets.
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Graph embedding techniques, applications, and performance: A survey
Goyal, P., & Ferrara, E. (2018) · Knowledge-Based Systems · doi:10.1016/j.knosys.2018.03.022 · Cited by 1,801
The power of protein interaction networks for associating genes with diseases
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Self-citationComputational approaches for detecting protein complexes from protein interaction networks: a survey
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Summarizing static and dynamic big graphs
Khan, A., Bhowmick, S. S., & Bonchi, F. (2017) · Proceedings of the VLDB Endowment · doi:10.14778/3137765.3137825 · Cited by 45
Metadata
- Authors
- Navlakha, S., Rastogi, R., & Shrivastava, N.
- Venue
- Proceedings of the 2008 ACM SIGMOD international conference on Management of data
- Publisher
- ACM
- Published
- 2008-06-09
- Pages
- 419-432
- ISSN
- 0730-8078 (ISSN-L)
- OpenAlex
- W2108781142
- Cited by
- 347
- References
- 22
- FWCI
- 13.09
- Type
- conference paper
- Language
- en
- Open access
- closed
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- Navlakha, S., Rastogi, R., & Shrivastava, N. (2008). Graph summarization with bounded error. Proceedings of the 2008 ACM SIGMOD international conference on Management of data. https://doi.org/10.1145/1376616.1376661
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