Hypergraph Drawing by Force-Directed Placement

Bressan, Stéphane

doi:10.1007/978-3-319-64471-4_31

Cited by 19 publications

(18 citation statements)

References 17 publications

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“…The dense composite graph data was simplified by preserving only the top 10% weighted edges, and compiled using the NetworkX package ( Hagberg et al, 2008 ). To visualize the composite similarity network, the pruned graph was projected onto a two-dimensional plane using the Fruchterman-Reingold force-directed algorithm ( Arafat and Bressan, 2017 ).…”

Section: Methodsmentioning

confidence: 99%

Spatiotemporal localization of proteins in mycobacteria

et al. 2021

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Section: Methodsmentioning

confidence: 99%

Spatiotemporal localization of proteins in mycobacteria

et al. 2021

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“…Node degree was used to size nodes and colours were used to mark different types of nodes. The Fruchterman Reingold (FR) algorithm (Fruchterman & Reingold, 2017) organized the nodes using a gravity approach where the higher the degree of a node the stronger is the force by which it attracts the linked nodes and pushes the unlinked ones. In order to enable visual comparisons, the parameters used on the FR algorithm, namely the area used to display the nodes, the gravity force and the speed at which changes occurred until the stabilization of the algorithm, were set equal for all networks From networks topology were extracted centrality measures (Bollobás, 2001;M.…”

Section: Methodsmentioning

confidence: 99%

Dopaminergic Gene Dosage in Autism versus Developmental Delay: From Complex Networks to Machine Learning approaches

Santos

Caramelo

Melo

2020

Preprint

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we studied genetic alterations related to dopamine processes that could impact brain development and function of 2636 participants. On average, from a genetic sample we were able to correctly separate autism from developmental delay with an accuracy of 85%. AbstractThe neural basis of behavioural changes in Autism Spectrum Disorders (ASD) remains a controversial issue. One factor contributing to this challenge is the phenotypic heterogeneity observed in ASD, which suggests that several different system disruptions may contribute to diverse patterns of impairment between and within study samples. Here, we took a retrospective approach, using SFARI data to study ASD by focusing on participants with genetic imbalances targeting the dopaminergic system. Using complex network analysis, we investigated the relations between participants, Gene Ontology (GO) and gene dosage related to dopaminergic neurotransmission from a polygenic point of view. We converted network analysis into a machine learning binary classification problem to differentiate ASD diagnosed participants from DD (developmental delay) diagnosed participants. Using 1846 participants to train a Random Forest algorithm, our best classifier achieved on average a diagnosis predicting accuracy of 85.18% (sd 1.11%) on a test sample of 790 participants using gene dosage features. In addition, we observed that if the classifier uses GO features it was also able to infer a correct response based on the previous examples because it is tied to a set of biological process, molecular functions and cellular components relevant to the problem. This yields a less variable and more compact set of features when comparing with gene dosage classifiers. Other facets of knowledge-based systems approaches addressing ASD through network analysis and machine learning, providing an interesting avenue of research for the future, are presented through the study.

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“…No graph visualization is used and relationships-such as co-authorship-are reduced to one-to-one relationships. Arafat and Bressan visually identify hyperedges by enclosing vertices in closed curves [24]. To process them, they transform hypergraphs into graphs using several algorithms.…”

Section: Visualization Of Hypergraphsmentioning

confidence: 99%

Analyzing Dynamic Hypergraphs with Parallel Aggregated Ordered Hypergraph Visualization

Valdivia

Buono

Plaisant

et al. 2021

IEEE Trans. Visual. Comput. Graphics

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Parallel Aggregated Ordered Hypergraph (PAOH) is a novel technique to visualize dynamic hypergraphs. Hypergraphs are a generalization of graphs where edges can connect several vertices. Hypergraphs can be used to model networks of business partners or co-authorship networks with multiple authors per article. A dynamic hypergraph evolves over discrete time slots. PAOH represents vertices as parallel horizontal bars and hyperedges as vertical lines, using dots to depict the connections to one or more vertices. We describe a prototype implementation of Parallel Aggregated Ordered Hypergraph, report on a usability study with 9 participants analyzing publication data, and summarize the improvements made. Two case studies and several examples are provided. We believe that PAOH is the first technique to provide a highly readable representation of dynamic hypergraphs. It is easy to learn and well suited for medium size dynamic hypergraphs (50-500 vertices) such as those commonly generated by digital humanities projects-our driving application domain.

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Hypergraph Drawing by Force-Directed Placement

Cited by 19 publications

References 17 publications

Spatiotemporal localization of proteins in mycobacteria

Spatiotemporal localization of proteins in mycobacteria

Dopaminergic Gene Dosage in Autism versus Developmental Delay: From Complex Networks to Machine Learning approaches

Analyzing Dynamic Hypergraphs with Parallel Aggregated Ordered Hypergraph Visualization

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