Learning Signed Graphs from Data

Matz, Gerald; Dittrich, Thomas

doi:10.1109/icassp40776.2020.9053083

Cited by 8 publications

(4 citation statements)

References 29 publications

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“…All of the previous works learn unsigned graphs with the exception of [36], where a signed graph is learned by employing signed graph Laplacian defined in [37]. By using signed Laplacian, [36] aims to learn positive edges between nodes whose signal values are similar and negative edges between nodes whose signal values have opposite signs with similar absolute values. However, this approach is not suitable when graph signals are either all positive-or negative-valued, as in the case of gene expression data.…”

Section: Graph Learningmentioning

confidence: 99%

“…Different variations of these frameworks to handle missing values and sparse outliers in the graph signals were considered in [31,32,33,34,35]. All of the previous works learn unsigned graphs with the exception of [36], where a signed graph is learned by employing signed graph Laplacian defined in [37]. By using signed Laplacian, [36] aims to learn positive edges between nodes whose signal values are similar and negative edges between nodes whose signal values have opposite signs with similar absolute values.…”

Section: Graph Learningmentioning

confidence: 99%

“…Di↵erent variations of this framework with constraints on the learned topology and for handling noisy graph signals were considered in (Kalofolias, 2016;Hou et al, 2016;Berger et al, 2020;Kadambari and Chepuri, 2020;Rui et al, 2017). All of the previous works learn unsigned graphs with the exception of (Matz and Dittrich, 2020), where a signed graph is learned by employing signed graph Laplacian defined by Kunegis et al (2010). By using signed Laplacian, Matz and Dittrich (2020) aim to learn positive edges between nodes whose signal values are similar and negative edges between nodes whose signal values have opposite signs with similar absolute values.…”

Section: Introductionmentioning

confidence: 99%

“…All of the previous works learn unsigned graphs with the exception of (Matz and Dittrich, 2020), where a signed graph is learned by employing signed graph Laplacian defined by Kunegis et al (2010). By using signed Laplacian, Matz and Dittrich (2020) aim to learn positive edges between nodes whose signal values are similar and negative edges between nodes whose signal values have opposite signs with similar absolute values.…”

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

scSGL: Signed Graph Learning for Single-Cell Gene Regulatory Network Inference

Karaaslanli

Saha

Aviyente

et al. 2021

Preprint

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Elucidating the topology of gene regulatory networks (GRN) from large single-cell RNA sequencing (scRNAseq) datasets, while effectively capturing its inherent cell-cycle heterogeneity, is currently one of the most pressing problems in computational systems biology. Recently, graph learning (GL) approaches based on graph signal processing (GSP) have been developed to infer graph topology from signals defined on graphs. However, existing GL methods are not suitable for learning signed graphs, which represent a characteristic feature of GRNs, as they account for both activating and inhibitory relationships between genes. To this end, we propose a novel signed GL approach, scSGL, that incorporates the similarity and dissimilarity between observed gene expression data to construct gene networks. The proposed approach is formulated as a non-convex optimization problem and solved using an efficient ADMM framework. In our experiments on simulated and real single cell datasets, scSGL compares favorably with other single cell gene regulatory network reconstruction algorithms.

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Section: Graph Learningmentioning

confidence: 99%