@inproceedings{78016c2ac5fa4c618522a93da7ac083a,
title = "Unified and contrasting cuts in multiple graphs: Application to medical imaging segmentation",
abstract = "The analysis of data represented as graphs is common having wide scale applications from social networks to medical imaging. A popular analysis is to cut the graph so that the disjoint subgraphs can represent communities (for social network) or background and foreground cognitive activity (for medical imaging). An emerging setting is when multiple data sets (graphs) exist which opens up the opportunity for many new questions. In this paper we study two such questions: i) For a collection of graphs find a single cut that is good for all the graphs and ii) For two collections of graphs find a single cut that is good for one collection but poor for the other. We show that existing formulations of multiview, consensus and alternative clustering cannot address these questions and instead we provide novel formulations in the spectral clustering framework. We evaluate our approaches on functional magnetic resonance imaging (fMRI) data to address questions such as: {"}What common cognitive network does this group of individuals have?{"} and {"}What are the differences in the cognitive networks for these two groups?{"} We obtain useful results without the need for strong domain knowledge.",
keywords = "Application, FMRI, Graph cuts",
author = "Kuo, {Chia Tung} and Xiang Wang and Peter Walker and Owen Carmichael and Jieping Ye and Ian Davidson",
note = "Publisher Copyright: {\textcopyright} 2015 ACM.; 21st ACM SIGKDD Conference on Knowledge Discovery and Data Mining, KDD 2015 ; Conference date: 10-08-2015 Through 13-08-2015",
year = "2015",
month = aug,
day = "10",
doi = "10.1145/2783258.2783318",
language = "English (US)",
series = "Proceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining",
publisher = "Association for Computing Machinery",
pages = "617--626",
booktitle = "KDD 2015 - Proceedings of the 21st ACM SIGKDD Conference on Knowledge Discovery and Data Mining",
}