Abstract
We study the problem of discovering robustly connected subgraphs that have simple descriptions. Our aim is, hence, to discover vertex sets which not only a) induce a subgraph that is difficult to fragment into disconnected components, but also b) can be selected from the entire graph using just a simple conjunctive query on their vertex attributes. Since many subgraphs do not have such a simple logical description, first mining robust subgraphs and post-hoc discovering their description leads to sub-optimal results. Instead, we propose to optimise over describable subgraphs only. To do so efficiently we propose a non-redundant iterative deepening approach, which we equip with a linear-time tight optimistic estimator that allows pruning large parts of the search space. Extensive empirical evaluation shows that our method can handle large real-world graphs, and discovers easily interpretable and meaningful subgraphs.
Original language | English |
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Title of host publication | Proceedings - 19th IEEE International Conference on Data Mining, ICDM 2019 |
Editors | Jianyong Wang, Kyuseok Shim, Xindong Wu |
Publisher | Institute of Electrical and Electronics Engineers Inc. |
Pages | 1150-1155 |
Number of pages | 6 |
ISBN (Electronic) | 9781728146034 |
DOIs | |
State | Published - Nov 2019 |
Externally published | Yes |
Event | 19th IEEE International Conference on Data Mining, ICDM 2019 - Beijing, China Duration: 8 Nov 2019 → 11 Nov 2019 |
Publication series
Name | Proceedings - IEEE International Conference on Data Mining, ICDM |
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Volume | 2019-November |
ISSN (Print) | 1550-4786 |
Conference
Conference | 19th IEEE International Conference on Data Mining, ICDM 2019 |
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Country/Territory | China |
City | Beijing |
Period | 8/11/19 → 11/11/19 |
Bibliographical note
Publisher Copyright:© 2019 IEEE.
Keywords
- Dense subgraph mining
- K core decomposition
- Pattern mining
- Robust connectedness
- Subgroup discovery
ASJC Scopus subject areas
- General Engineering