The State of the Art in Multilayer Network Visualization

McGee, Fintan; Ghoniem, Mohammad; Mélançon, Guy; Otjacques, Benoît; Pinaud, Bruno

doi:10.1111/cgf.13610

Cited by 72 publications

(39 citation statements)

References 145 publications

(343 reference statements)

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“…Visualization plays a key role in data mining tasks. Although many computational platforms, such as Cytoscape (Shannon et al, 2003), have been developed for the network visualization, few tools are available for efficient and intuitive visualization of HMLN, especially when the network is large (Mcgee et al, 2019). There is an urgent need to design a robust data structure for the representation and grouping of nodes and relations in HMLN in a way that they can be efficiently mapped to the graphic user interface and easily navigated by users.…”

Section: Visualizing Hmlnmentioning

confidence: 99%

Heterogeneous Multi-Layered Network Model for Omics Data Integration and Analysis

et al. 2020

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Advances in next-generation sequencing and high-throughput techniques have enabled the generation of vast amounts of diverse omics data. These big data provide an unprecedented opportunity in biology, but impose great challenges in data integration, data mining, and knowledge discovery due to the complexity, heterogeneity, dynamics, uncertainty, and high-dimensionality inherited in the omics data. Network has been widely used to represent relations between entities in biological system, such as protein-protein interaction, gene regulation, and brain connectivity (i.e. network construction) as well as to infer novel relations given a reconstructed network (aka link prediction). Particularly, heterogeneous multi-layered network (HMLN) has proven successful in integrating diverse biological data for the representation of the hierarchy of biological system. The HMLN provides unparalleled opportunities but imposes new computational challenges on establishing causal genotype-phenotype associations and understanding environmental impact on organisms. In this review, we focus on the recent advances in developing novel computational methods for the inference of novel biological relations from the HMLN. We first discuss the properties of biological HMLN. Then we survey four categories of state-ofthe-art methods (matrix factorization, random walk, knowledge graph, and deep learning). Thirdly, we demonstrate their applications to omics data integration and analysis. Finally, we outline strategies for future directions in the development of new HMLN models.

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Section: Visualizing Hmlnmentioning

confidence: 99%

Heterogeneous Multi-Layered Network Model for Omics Data Integration and Analysis

et al. 2020

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“…After having considered some of the properties of this class of Tensor Markov Fields, it may become evident that aside from purely theoretical importance, there is a number of important applications that may arise as probabilistic graphical models in tensor valued problems, among the ones that are somewhat evident are the following: The analysis of multidimensional biomolecular networks such as the ones arising from multi-omic experiments (For a real-life example, see Figure 4 ) [ 8 , 9 , 10 ]; Probabilistic graphical models in computer vision (especially 3D reconstructions and 4D [3D+time] rendering) [ 11 ]; The study of fracture mechanics in continuous deformable media [ 12 ]; Probabilistic network models for seismic dynamics [ 13 ]; Boolean networks in control theory [ 14 ]. …”

Section: Specific Applicationsmentioning

confidence: 99%

“…Probabilistic graphical models in computer vision (especially 3D reconstructions and 4D [3D+time] rendering) [ 11 ];…”

Section: Specific Applicationsmentioning

confidence: 99%

On a Class of Tensor Markov Fields

Hernández‐Lemus

2020

Entropy

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Here, we introduce a class of Tensor Markov Fields intended as probabilistic graphical models from random variables spanned over multiplexed contexts. These fields are an extension of Markov Random Fields for tensor-valued random variables. By extending the results of Dobruschin, Hammersley and Clifford to such tensor valued fields, we proved that tensor Markov fields are indeed Gibbs fields, whenever strictly positive probability measures are considered. Hence, there is a direct relationship with many results from theoretical statistical mechanics. We showed how this class of Markov fields it can be built based on a statistical dependency structures inferred on information theoretical grounds over empirical data. Thus, aside from purely theoretical interest, the Tensor Markov Fields described here may be useful for mathematical modeling and data analysis due to their intrinsic simplicity and generality.

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“…Various network visualization systems support basic operations, such as aggregating nodes into supernodes, or filtering nodes and edges. We limit our discussion here to tools and techniques that support more sophisticated wrangling operations and refer to review articles [35,43] for a survey of more general network visualization methods.…”

Section: Related Workmentioning

confidence: 99%

Origraph: Interactive Network Wrangling

Bigelow

Nobre

Meyer

et al. 2019

2019 IEEE Conference on Visual Analytics Science and Technology (VAST)

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Figure 1: Overview of the Origraph UI. The network model view shows relationships between node and edge classes and is the primary interface for operations related to connectivity. The attribute view shows node and edge attributes in a table and is the primary interface for attribute-related operations. The network sample view visualizes a preview of the current state of the network. ABSTRACTNetworks are a natural way of thinking about many datasets. The data on which a network is based, however, is rarely collected in a form that suits the analysis process, making it necessary to create and reshape networks. Data wrangling is widely acknowledged to be a critical part of the data analysis pipeline, yet interactive network wrangling has received little attention in the visualization research community. In this paper, we discuss a set of operations that are important for wrangling network datasets and introduce a visual data wrangling tool, Origraph, that enables analysts to apply these operations to their datasets. Key operations include creating a network from source data such as tables, reshaping a network by introducing new node or edge classes, filtering nodes or edges, and deriving new node or edge attributes. Our tool, Origraph, enables analysts to execute these operations with little to no programming, and to immediately visualize the results. Origraph provides views to investigate the network model, a sample of the network, and node and edge attributes. In addition, we introduce interfaces designed to aid analysts in specifying arguments for sensible network wrangling operations. We demonstrate the usefulness of Origraph in two Use Cases: first, we investigate gender bias in the film industry, and then the influence of money on the political support for the war in Yemen. [Graph-based database models]: -the way an analyst thinks about it. To model data as a network, analysts must wrangle the dataset, often starting with tabular or key-value data. Transforming data itself can lead to new hypotheses, and thus a new network representation of the data. Also, new tasks often necessitate new data abstractions [40]. It stands to reason that the ability to rapidly and easily transform network data can foster creative visualization solutions and simplify both exploration and communication of the key aspects of a dataset. Existing network wrangling tools, most notably Ploceus and Orion [21,33], focus on creating an initial network model, but no tools yet exist to iteratively and interactively reshape the network model itself with operations such as converting between nodes and edges [41]. Other operations that leverage edges, such as arXiv:1812.06337v3 [cs.HC]

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The State of the Art in Multilayer Network Visualization

Cited by 72 publications

References 145 publications

Heterogeneous Multi-Layered Network Model for Omics Data Integration and Analysis

Heterogeneous Multi-Layered Network Model for Omics Data Integration and Analysis

On a Class of Tensor Markov Fields

Origraph: Interactive Network Wrangling

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