Relational models for generating labeled real-world graphs

Lippert, C; Shervashidze, N; Stegle, O

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Relational models for generating labeled real-world graphs

Lippert, C., Shervashidze, N., & Stegle, O. (2009). Relational models for generating labeled real-world graphs. In 7th International Workshop on Mining and Learning with Graphs (MLG 2009).

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Item Permalink: https://hdl.handle.net/21.11116/0000-0010-5B29-B Version Permalink: https://hdl.handle.net/21.11116/0000-0010-5B2A-A

Genre: Conference Paper

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https://dtai.cs.kuleuven.be/events/ilp-mlg-srl/USBStick/papers/MLG09-2.pdf (Any fulltext) Open Access status unknown

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Creators:
Lippert, C¹, Author
Shervashidze, N¹, Author
Stegle, O¹, Author

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1Department Molecular Biology, Max Planck Institute for Developmental Biology, Max Planck Society, ou_3375790

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Abstract: Analyzing and understanding the structure of social networks and other real-world graphs has become a major area of research in the field of data mining. An important problem setting is the creation of realistic synthetic graphs that resemble realworld social networks. While a range of efficient algorithms for this task have been proposed, current methods solely take the network topology into account ignoring any node labels. We propose a probabilistic approach to synthetic graph generation with node labels, building on concepts from relational learning.

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Dates: Published Online: 2009-07

Publication Status: Published online

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Title: 7th International Workshop on Mining and Learning with Graphs (MLG 2009)

Place of Event: Leuven, Belgium

Start-/End Date: 2009-07-02 - 2009-07-04

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Title: 7th International Workshop on Mining and Learning with Graphs (MLG 2009)

Source Genre: Proceedings

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Pages: 3 Volume / Issue: - Sequence Number: - Start / End Page: - Identifier: -