Change of room: The workshop is now scheduled to take place in ROOM 3.

Presentations for accepted submissions will be allotted 15 minutes each.

There are also two posters invited from the main conference poster strack, these are allotted 2 minutes each.

Session 1: Human factors for multilayer networks

A Study of Matrix Representations for Networks with Multiple Edge Types

Athanasios Vogogias    Edinburgh Napier University, Edinburgh, United Kingdom
Daniel Archambault    Swansea University, Swansea, United Kingdom
Benjamin Bach    School of Informatics, Edinburgh University, Edinburgh, United Kingdom
Prof Jessie B Kennedy    Edinburgh Napier University, Edinburgh, United Kingdom

Abstract

This paper reports on a formal experiment on visual encodings of networks with multiple types of edges. Multilayer networks are important for representing relationships in complex systems in disciplines such as biology, ecology, social networks and software engineering. Our tasks and experimental conditions inspired by real problems in biology apply to multilayer networks and include type lookup, type frequency, and comparison within the same as well as across networks. We focus on encodings in adjacency matrices based on three visual variables of orientation, position and colour for our study. We found that the encodings performed differently depending on the task. However, colour was found to help in all tasks except type lookup tasks. Orientation generally outperformed position in all of our tasks.

Human Factors and Multilayer Networks

Margit Pohl    Institute of Visual Computing and Human-Centered Technologies, Vienna University of Technology, Vienna, Austria
Prof. Dr. Andreas Kerren    Department of Computer Science and Media Technology, Linnaeus University, Växjö, Kronoberg, Sweden

Abstract

Analysts of specific application domains, such as experts in systems biology or social scientists, are often interested to visually analyze a number of different network structures in conjunction, for example by using various visual structures of so-called multilayer networks. From the perspective of the human  nalyst, a sufficient perception and, consequently, a good understanding of those visual representations of multilayer networks is a non-trivial and often challenging task. Despite this practical importance and the clearly interesting visualization challenges, only few evaluation studies exist that investigate usability and cognitive issues of complex networks or, more specifically, multilayer networks. In this position paper, we address two main goals. On the one hand, we discuss existing studies from the fields of human-computer interaction and cognitive psychology that could inform the designers of multilayer network  visualization in the future. On the other hand, we formulate first tentative recommendations for the design of multilayer networks, identify open issues in this context, and clarify possible future directions of research.

Invited Poster: TaMax: Visualizing Dense Multivariate Graphs with Adjacency Matrices

Ilkin Safarli, Alexander Lex

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Session 2: Multilayer network modelling for visualization

Graph Models for Biological Pathway Visualization: State of the Art and Future Challenges

Hsiang-Yun Wu    Institute of Visual Computing and Human-Centered Technology, TU Wien, Vienna, Austria
Martin Nöllenburg    Algorithms and Complexity Group, TU Wien, Vienna, Austria
Ivan Viola    Visual Computing Center, King Abdullah University of Science and Technology, Thuwal-Jeddah, Saudi Arabia

Abstract

The concept of multilayer networks has become recently integrated into complex systems modeling since it encapsulates a very general concept of complex relationships. Biological pathways are an example of complex real-world networks, where vertices represent biological entities, and edges indicate the underlying connectivity. For this reason, using multilayer networks to model biological knowledge allows us to formally cover essential properties and theories in the field, which also raises challenges in visualization. This is because, in the early days of pathway visualization research, only restricted types of graphs, such as simple graphs, clustered graphs, and others were adopted. In this paper, we revisit a heterogeneous definition of biological networks and aim to provide an overview to see the gaps between data modeling and visual representation. The contribution will, therefore, lie in providing guidelines and challenges of using multilayer networks as a unified data structure for the biological pathway visualization.

Layer Definition and Discovery in Multilayer Network Datasets

Fintan McGee    Environmental Informatics Group, Luxembourg Institute of Science and Technology (LIST), Esch/Alzette, Luxembourg
Ludovic Morin    Environmental Informatics Group, Luxembourg Institute of Science and Technology (LIST), Esch/Alzette, Luxembourg
Mickaël Stefas    Environmental Informatics Group, Luxembourg Institute of Science and Technology (LIST), Esch/Alzette, Luxembourg
Simone Zorzan    Environmental Informatics Group, Luxembourg Institute of Science and Technology (LIST), Esch/Alzette, Luxembourg
Mohammad Ghoniem    Environmental Informatics Group, Luxembourg Institute of Science and Technology (LIST), Esch/Alzette, Luxembourg

Abstract

The real world systems modelled by multilayer networks are frequently characterised by a high level of complexity.  Understanding the most suitable definition of layer that can help a user is a significant challenge. Due to the scale of many datasets,
even finding the entities and relationships that are of interest to the user is difficult. In this work we describe the issues with  working with large multilayer data sources, based on the development of a multilayer network visualization prototype.  We present an in-progress prototype and discuss future research directions for creating multilayer networks visualization of data extracted from massive sources.

Invited Poster: Explorative Visualization for Phenotypic-Genotypic Network

Hiroaki Natsukawa, Shungo Ishino, Yosuke Onoue, Koji Kyoda, Shuichi Onami, Koji Koyamada

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