Abstract
Graph clustering aims at discovering a natural grouping of the nodes such that similar nodes are assigned to a common cluster. Many different algorithms have been proposed in the literature: for simple graphs, for graphs with attributes associated to nodes, and for graphs where edges represent different types of relations among nodes. However, complex data in many domains can be represented as both attributed and multi-relational networks.
In this paper, we propose SpectralMix, a joint dimensionality reduction technique for multi-relational graphs with categorical node attributes. SpectralMix integrates all information available from the attributes, the different types of relations, and the graph structure to enable a sound interpretation of the clustering results. Moreover, it generalizes existing techniques: it reduces to spectral embedding and clustering when only applied to a single graph and to homogeneity analysis when applied to categorical data.
Experiments conducted on several real-world datasets enable us to detect dependencies between graph structure and categorical attributes, moreover, they exhibit the superiority of SpectralMix over existing methods.
In this paper, we propose SpectralMix, a joint dimensionality reduction technique for multi-relational graphs with categorical node attributes. SpectralMix integrates all information available from the attributes, the different types of relations, and the graph structure to enable a sound interpretation of the clustering results. Moreover, it generalizes existing techniques: it reduces to spectral embedding and clustering when only applied to a single graph and to homogeneity analysis when applied to categorical data.
Experiments conducted on several real-world datasets enable us to detect dependencies between graph structure and categorical attributes, moreover, they exhibit the superiority of SpectralMix over existing methods.
| Original language | English |
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| Title of host publication | Proceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining |
| Place of Publication | New York, NY |
| Publisher | Association for Computing Machinery (ACM) |
| Pages | 1431-1440 |
| Number of pages | 10 |
| ISBN (Electronic) | 978-1-4503-8332-5 |
| DOIs | |
| Publication status | Published - 14 Aug 2021 |
| Event | 27th ACM SIGKDD Conference on Knowledge Discovery & Data Mining (KDD) - , Singapore Duration: 14 Aug 2021 → 18 Aug 2021 |
Conference
| Conference | 27th ACM SIGKDD Conference on Knowledge Discovery & Data Mining (KDD) |
|---|---|
| Country/Territory | Singapore |
| Period | 14/08/21 → 18/08/21 |
Austrian Fields of Science 2012
- 102033 Data mining
Keywords
- Attributed graphs
- Graph embedding
- Multi-relational graphs
- Spectral clustering
- multi-relational graphs
- attributed graphs
- graph embedding
- spectral clustering