Strength Adjusted Multilayer Spectral Clustering

Karaaslanli, Abdullah; Aviyente, Selin

doi:10.1109/mlsp.2019.8918845

Cited by 4 publications

(2 citation statements)

References 28 publications

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“…Different works have presented results on spectral clustering on multi-layer networks [2,6,12], while very few, to the best of our knowledge, investigate spectral clustering methods on attributed graphs [27]. Whereas, there are methods not designed for graphs, which exploit the fusion of the cluster-separation information from all eigenvectors to achieve a better clustering [36] or perform clustering of numerical and categorical data based on homogeneity analysis [23].…”

Section: Related Workmentioning

confidence: 99%

Spectral Clustering of Attributed Multi-relational Graphs

Sadikaj

Velaj

Behzadi

et al. 2021

Proceedings of the 27th ACM SIGKDD Conference on Knowledge Discovery &Amp; Data Mining

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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. CCS CONCEPTS• Theory of computation → Unsupervised learning and clustering.

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Section: Related Workmentioning

confidence: 99%

Spectral Clustering of Attributed Multi-relational Graphs

Sadikaj

Velaj

Behzadi

et al. 2021

Proceedings of the 27th ACM SIGKDD Conference on Knowledge Discovery &Amp; Data Mining

View full text Add to dashboard Cite

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“…In the first setup, multiple datasets are given and each dataset is defined on a view [26], [35]. On the other hand, the second setup deals with the mixture of graph signals, where one is given a single dataset and the association of graph signals to the views is not known [36], [37], [38]. The focus of the present paper is the first category.…”

Section: Related Workmentioning

confidence: 99%

Multiview Graph Learning for single-cell RNA sequencing data

Karaaslanli

Saha

Aviyente

et al. 2021

Preprint

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Characterizing the underlying topology of gene regulatory networks is one of the fundamental problems of systems biology. Ongoing developments in high throughput sequencing technologies has made it possible to capture the expression of thousands of genes at the single cell resolution. However, inherent cellular heterogeneity and high sparsity of the single cell datasets render void the application of regular Gaussian assumptions for constructing gene regulatory networks. Additionally, most algorithms aimed at single cell gene regulatory network reconstruction, estimate a single network ignoring group-level (cell-type) information present within the datasets. To better characterize single cell gene regulatory networks under different but related conditions we propose the joint estimation of multiple networks using multiview graph learning (mvGL). The proposed method is developed based on recent works in graph signal processing (GSP) for graph learning, where graph signals are assumed to be smooth over the unknown graph structure. Graphs corresponding to the different datasets are regularized to be similar to each other through a learned consensus graph. We further kernelize mvGL with the kernel selected to suit the structure of single cell data. An efficient algorithm based on prox-linear block coordinate descent is used to optimize mvGL. We study the performance of mvGL using synthetic data generated with a diverse set of parameters. We further show that mvGL successfully identifies well-established regulators in a mouse embryonic stem cell differentiation study and a cancer clinical study of medulloblastoma.

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