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Detecting Connected Components and Communities in Hypergraphs
From this page you can download the algorithm for decomposing a
3-partite 3-uniform hypergraph stored in a database into its
normal and hyperincident connected components.
If you use this software, please cite:
| Citation key | Neubauer2009a0 |
|---|---|
| Author | Neubauer, N. and Obermayer, K. |
| Pages | 229 – 238 |
| Year | 2009 |
| DOI | http://doi.acm.org/10.1145/1592394.1592398 |
| Journal | ACM SIGIWEB Newsletter |
| Month | September |
| Publisher | Association for Computing Machinery |
| Abstract | Data created by social bookmarking systems can be described as 3-partite 3-uniform hypergraphs connecting documents, users, and tags (tagging networks), such that the toolbox of complex network analysis can be applied to examine their properties. One of the most basic tools, the analysis of connected components, however cannot be applied meaningfully: Tagging networks tend to be almost entirely connected. We therefore propose a generalization of connected components, m-hyperincident connected components. We show that decomposing tagging networks into 2-hyperincident connected components yields a characteristic component distribution with a salient giant component that can be found across various datasets. This pattern changes if the underlying formation process changes, for example, if the hypergraph is constructed from search logs, or if the tagging data is contaminated by spam: It turns out that the second- to 129th largest components of the spam-labeled Bibsonomy dataset are inhabited exclusively by spam users. Based on these ndings, we propose and unsupervised method for spam detection. |
| Bibtex Type of Publication | Selected:social |
Also, you can download several software packages for
multi-partite community detection in hypergraphs.
mpcd (Multi-Partite Community Detection)
performs community detection based on multi-partite modularity
optimization.
mpcb (Multi-Partite Community Benchmarking)
evaluates community detection algorithms based on three different
families of synthetic benchmark hypergraphs.
mpcb.zip, with data: mpcb_with_data.zip
mpce (Multi-Partite Community Exploration)
allows for the interactive exploration of community detection
results such as the ones provided by mpcd.
If you use this software, please cite:
| Citation key | Neubauer2009a0 |
|---|---|
| Author | Neubauer, N. and Obermayer, K. |
| Pages | 229 – 238 |
| Year | 2009 |
| DOI | http://doi.acm.org/10.1145/1592394.1592398 |
| Journal | ACM SIGIWEB Newsletter |
| Month | September |
| Publisher | Association for Computing Machinery |
| Abstract | Data created by social bookmarking systems can be described as 3-partite 3-uniform hypergraphs connecting documents, users, and tags (tagging networks), such that the toolbox of complex network analysis can be applied to examine their properties. One of the most basic tools, the analysis of connected components, however cannot be applied meaningfully: Tagging networks tend to be almost entirely connected. We therefore propose a generalization of connected components, m-hyperincident connected components. We show that decomposing tagging networks into 2-hyperincident connected components yields a characteristic component distribution with a salient giant component that can be found across various datasets. This pattern changes if the underlying formation process changes, for example, if the hypergraph is constructed from search logs, or if the tagging data is contaminated by spam: It turns out that the second- to 129th largest components of the spam-labeled Bibsonomy dataset are inhabited exclusively by spam users. Based on these ndings, we propose and unsupervised method for spam detection. |
| Bibtex Type of Publication | Selected:social |
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