000184978 001__ 184978
000184978 005__ 20181203023029.0
000184978 0247_ $$2doi$$a10.1109/TSP.2013.2295553
000184978 022__ $$a1053-587X
000184978 02470 $$2ISI$$a000332033600011
000184978 037__ $$aARTICLE
000184978 245__ $$aClustering on Multi-Layer Graphs via Subspace Analysis on Grassmann Manifolds
000184978 260__ $$aPiscataway$$bInstitute of Electrical and Electronics Engineers$$c2014
000184978 269__ $$a2014
000184978 300__ $$a14
000184978 336__ $$aJournal Articles
000184978 520__ $$aRelationships between entities in datasets are often of multiple nature, like geographical distance, social relationships, or common interests among people in a social network, for example. This information can naturally be modeled by a set of weighted and undirected graphs that form a global multi-layer graph, where the common vertex set represents the entities and the edges on different layers capture the similarities of the entities in term of the different modalities. In this paper, we address the problem of analyzing multi-layer graphs and propose methods for clustering the vertices by efficiently merging the information provided by the multiple modalities. To this end, we propose to combine the characteristics of individual graph layers using tools from subspace analysis on a Grassmann manifold. The resulting combination can then be viewed as a low dimensional representation of the original data which preserves the most important information from diverse relationships between entities. We use this information in new clustering methods and test our algorithm on several synthetic and real world datasets where we demonstrate superior or competitive performances compared to baseline and state-of-the-art techniques. Our generic framework further extends to numerous analysis and learning problems that involve different types of information on graphs.
000184978 6531_ $$amulti-layer graphs
000184978 6531_ $$asubspace representation
000184978 6531_ $$aGrassmann manifold
000184978 6531_ $$aclustering
000184978 700__ $$0242933$$aDong, Xiaowen$$g193962
000184978 700__ $$0241061$$aFrossard, Pascal$$g101475
000184978 700__ $$0240428$$aVandergheynst, Pierre$$g120906
000184978 700__ $$aNefedov, Nikolai
000184978 773__ $$j62$$k4$$q905-918$$tIEEE Transactions on Signal Processing
000184978 8564_ $$uhttp://arxiv.org/abs/1303.2221$$zURL
000184978 909C0 $$0252392$$pLTS2$$xU10380
000184978 909C0 $$0252393$$pLTS4$$xU10851
000184978 909CO $$ooai:infoscience.tind.io:184978$$pSTI$$pGLOBAL_SET$$particle
000184978 917Z8 $$x193962
000184978 917Z8 $$x193962
000184978 917Z8 $$x193962
000184978 937__ $$aEPFL-ARTICLE-184978
000184978 973__ $$aEPFL$$rREVIEWED$$sPUBLISHED
000184978 980__ $$aARTICLE