ML Anthology

Multi-Graph Clustering Based on Interior-Node Topology with Applications to Brain Networks

Guixiang Ma, Lifang He, Bokai Cao, Jiawei Zhang, Philip S. Yu, Ann B. Ragin
ECML-PKDD 2016 pp. 476-492
doi:10.1007/978-3-319-46128-1_30 /ecmlpkdd/2016/ma2016ecmlpkdd-multigraph/

Abstract

Learning from graph data has been attracting much attention recently due to its importance in many scientific applications, where objects are represented as graphs. In this paper, we study the problem of multi-graph clustering ( i.e. , clustering multiple graphs). We propose a multi-graph clustering approach (MGCT) based on the interior-node topology of graphs. Specifically, we extract the interior-node topological structure of each graph through a sparsity-inducing interior-node clustering. We merge the interior-node clustering stage and the multi-graph clustering stage into a unified iterative framework, where the multi-graph clustering will influence the interior-node clustering and the updated interior-node clustering results will be further exerted on multi-graph clustering. We apply MGCT on two real brain network data sets ( i.e. , ADHD and HIV). Experimental results demonstrate the superior performance of the proposed model on multi-graph clustering.

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Cite

Text

Ma et al. "Multi-Graph Clustering Based on Interior-Node Topology with Applications to Brain Networks." European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases, 2016. doi:10.1007/978-3-319-46128-1_30

Markdown

[Ma et al. "Multi-Graph Clustering Based on Interior-Node Topology with Applications to Brain Networks." European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases, 2016.](https://mlanthology.org/ecmlpkdd/2016/ma2016ecmlpkdd-multigraph/) doi:10.1007/978-3-319-46128-1_30

BibTeX

@inproceedings{ma2016ecmlpkdd-multigraph,
  title     = {{Multi-Graph Clustering Based on Interior-Node Topology with Applications to Brain Networks}},
  author    = {Ma, Guixiang and He, Lifang and Cao, Bokai and Zhang, Jiawei and Yu, Philip S. and Ragin, Ann B.},
  booktitle = {European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases},
  year      = {2016},
  pages     = {476-492},
  doi       = {10.1007/978-3-319-46128-1_30},
  url       = {https://mlanthology.org/ecmlpkdd/2016/ma2016ecmlpkdd-multigraph/}
}