[ { "id": "ehlers-2025-ait", "type_id": "journalpaper_notalk", "tu_id": null, "repositum_id": "20.500.12708/208050", "title": "An introduction to and survey of biological network visualization", "date": "2025-02", "abstract": "Biological networks describe complex relationships in biological systems, which represent biological entities as vertices and their underlying connectivity as edges. Ideally, for a complete analysis of such systems, domain experts need to visually integrate multiple sources of heterogeneous data, and visually, as well as numerically, probe said data in order to explore or validate (mechanistic) hypotheses. Such visual analyses require the coming together of biological domain experts, bioinformaticians, as well as network scientists to create useful visualization tools. Owing to the underlying graph data becoming ever larger and more complex, the visual representation of such biological networks has become challenging in its own right. This introduction and survey aims to describe the current state of biological network visualization in order to identify scientific gaps for visualization experts, network scientists, bioinformaticians, and domain experts, such as biologists, or biochemists, alike. Specifically, we revisit the classic visualization pipeline, upon which we base this paper’s taxonomy and structure, which in turn forms the basis of our literature classification. This pipeline describes the process of visualizing data, starting with the raw data itself, through the construction of data tables, to the actual creation of visual structures and views, as a function of task-driven user interaction. Literature was systematically surveyed using API-driven querying where possible, and the collected papers were manually read and categorized based on the identified sub-components of this visualization pipeline’s individual steps. From this survey, we highlight a number of exemplary visualization tools from multiple biological sub-domains in order to explore how they adapt these discussed techniques and why. Additionally, this taxonomic classification of the collected set of papers allows us to identify existing gaps in biological network visualization practices. We finally conclude this report with a list of open challenges and potential research directions. Examples of such gaps include (i) the overabundance of visualization tools using schematic or straight-line node-link diagrams, despite the availability of powerful alternatives, or (ii) the lack of visualization tools that also integrate more advanced network analysis techniques beyond basic graph descriptive statistics.", "authors_et_al": false, "substitute": null, "main_image": null, "sync_repositum_override": "projects", "repositum_presentation_id": null, "authors": [ 1850, 5426, 1249, 1579, 5427, 5428, 5251, 1464 ], "articleno": "104115", "doi": "10.1016/j.cag.2024.104115", "issn": "1873-7684", "journal": "COMPUTERS & GRAPHICS-UK", "pages": "31", "publisher": "PERGAMON-ELSEVIER SCIENCE LTD", "volume": "126", "research_areas": [ "BioVis", "NetVis" ], "keywords": [ "Network visualization", "Visualization pipeline", "Sensemaking loop", "Visual analytics", "Network analysis", "Biological networks", "State-of-the-art-report" ], "weblinks": [], "files": [], "projects_workgroups": [ "vis" ], "url": "https://www.cg.tuwien.ac.at/research/publications/2025/ehlers-2025-ait/", "__class": "Publication" }, { "id": "ehlers-2024-mmm", "type_id": "journalpaper_notalk", "tu_id": null, "repositum_id": "20.500.12708/205726", "title": "Me! Me! Me! Me! A study and comparison of ego network representations", "date": "2024-12", "abstract": "From social networks to brain connectivity, ego networks are a simple yet powerful approach to visualizing parts of a larger graph, i.e. those related to a selected focal node — the so-called “ego”. While surveys and comparisons of general graph visualization approaches exist in the literature, we note (i) the many conflicting results of comparisons of adjacency matrices and node-link diagrams, thus motivating further study, as well as (ii) the absence of such systematic comparisons for ego networks specifically. In this paper, we propose the development of empirical recommendations for ego network visualization strategies. First, we survey the literature across application domains and collect examples of network visualizations to identify the most common visual encodings, namely straight-line, radial, and layered node-link diagrams, as well as adjacency matrices. These representations are then applied to a representative, intermediate-sized network and subsequently compared in a large-scale, crowd-sourced user study in a mixed-methods analysis setup to investigate their impact on both user experience and performance. Within the limits of this study, and contrary to previous comparative investigations of adjacency matrices and node-link diagrams (outside of ego networks specifically), participants performed systematically worse when using adjacency matrices than those using node-link diagrammatic representations. Similar to previous comparisons of different node-link diagrams, we do not detect any notable differences in participant performance between the three node-link diagrams. Lastly, our quantitative and qualitative results indicate that participants found adjacency matrices harder to learn, use, and understand than node-link diagrams. We conclude that in terms of both participant experience and performance, a layered node-link diagrammatic representation appears to be the most preferable for ego network visualization purposes.", "authors_et_al": false, "substitute": null, "main_image": null, "sync_repositum_override": "projects", "repositum_presentation_id": null, "authors": [ 1850, 1813, 5417, 1464, 1410 ], "articleno": "104123", "doi": "10.1016/j.cag.2024.104123", "issn": "1873-7684", "journal": "COMPUTERS & GRAPHICS-UK", "open_access": "yes", "pages": "15", "pages_from": "1", "pages_to": "15", "publisher": "PERGAMON-ELSEVIER SCIENCE LTD", "volume": "125", "research_areas": [ "NetVis" ], "keywords": [ "Adjacency matrix", "Ego network visualization", "Layered node-link diagram", "Radial node-link diagram", "Straight-line node-link diagram", "User study" ], "weblinks": [], "files": [ { "description": null, "filetitle": "paper", "main_file": true, "use_in_gallery": false, "access": "public", "name": "ehlers-2024-mmm-paper.pdf", "type": "application/pdf", "size": 2475868, "path": "Publication:ehlers-2024-mmm", "url": "https://www.cg.tuwien.ac.at/research/publications/2024/ehlers-2024-mmm/ehlers-2024-mmm-paper.pdf", "thumb_image_sizes": [ 16, 64, 100, 175, 300, 600 ], "thumb_url": "https://www.cg.tuwien.ac.at/research/publications/2024/ehlers-2024-mmm/ehlers-2024-mmm-paper:thumb{{size}}.png" } ], "projects_workgroups": [ "vis" ], "url": "https://www.cg.tuwien.ac.at/research/publications/2024/ehlers-2024-mmm/", "__class": "Publication" }, { "id": "Klaus2024", "type_id": "bachelorthesis", "tu_id": null, "repositum_id": null, "title": "Multidimensional Clustering for Machine Data Analysis", "date": "2024-07-11", "abstract": "Machine data analysis is an important aspect in modern industrial facilities, as stakeholders want their machinery to be as efficient as possible. To this end, they utilize the IIoT, enabling the analysis of gathered machine data. To gain useful information through the aggregated data, Big Data analytics are invaluable to the domain experts conducting\nmachine data analysis. The insights gained through Big Data analytics allow for a better efficiency of the facility by enabling data-driven decisions.\nThis thesis sets out to explore the feasibility of multidimensional clustering for machine data analysis in a web-based environment. To do this, we developed an application that\ncombines statistical methods and several visualization techniques into a web interface.\nWe evaluated the tool based on its real-world applicability and performance. The developed application has produced promising results, when employed on multivariate time series from industrial machinery, and thereby provides a robust foundation for future improvements.", "authors_et_al": false, "substitute": null, "main_image": { "description": null, "filetitle": "image", "main_file": true, "use_in_gallery": true, "access": "public", "image_width": 917, "image_height": 520, "name": "Klaus2024-image.JPG", "type": "image/jpeg", "size": 79072, "path": "Publication:Klaus2024", "url": "https://www.cg.tuwien.ac.at/research/publications/2024/Klaus2024/Klaus2024-image.JPG", "thumb_image_sizes": [ 16, 64, 100, 175, 300, 600 ], "thumb_url": "https://www.cg.tuwien.ac.at/research/publications/2024/Klaus2024/Klaus2024-image:thumb{{size}}.png" }, "sync_repositum_override": null, "repositum_presentation_id": null, "authors": [ 5376 ], "date_end": "2024-07-11", "date_start": "2023-11-11", "matrikelnr": "12120487", "supervisor": [ 166 ], "research_areas": [ "NetVis" ], "keywords": [], "weblinks": [], "files": [ { "description": null, "filetitle": "Bachelor thesis", "main_file": true, "use_in_gallery": false, "access": "public", "name": "Klaus2024-Bachelor thesis.pdf", "type": "application/pdf", "size": 7283210, "path": "Publication:Klaus2024", "url": "https://www.cg.tuwien.ac.at/research/publications/2024/Klaus2024/Klaus2024-Bachelor thesis.pdf", "thumb_image_sizes": [ 16, 64, 100, 175, 300, 600 ], "thumb_url": "https://www.cg.tuwien.ac.at/research/publications/2024/Klaus2024/Klaus2024-Bachelor thesis:thumb{{size}}.png" }, { "description": null, "filetitle": "image", "main_file": true, "use_in_gallery": true, "access": "public", "image_width": 917, "image_height": 520, "name": "Klaus2024-image.JPG", "type": "image/jpeg", "size": 79072, "path": "Publication:Klaus2024", "url": "https://www.cg.tuwien.ac.at/research/publications/2024/Klaus2024/Klaus2024-image.JPG", "thumb_image_sizes": [ 16, 64, 100, 175, 300, 600 ], "thumb_url": "https://www.cg.tuwien.ac.at/research/publications/2024/Klaus2024/Klaus2024-image:thumb{{size}}.png" } ], "projects_workgroups": [ "vis" ], "url": "https://www.cg.tuwien.ac.at/research/publications/2024/Klaus2024/", "__class": "Publication" }, { "id": "ehlers-2024-vgs", "type_id": "inproceedings", "tu_id": null, "repositum_id": "20.500.12708/196069", "title": "Visualizing Group Structure in Compound Graphs: The Current State, Lessons Learned, and Outstanding Opportunities", "date": "2024-03-11", "abstract": "Compound graphs are common across domains, from social science to biochemical pathway studies, and their visualization is important to both their exploration and analysis. However, effectively visualizing a compound graph's topology and group structure requires careful consideration, as evident by the many different approaches to this particular problem. To better understand the current advancements in compound graph visualization, we have consolidated and streamlined existing surveys' taxonomies. More specifically, we aim to disentangle the visual relationship between graph topology and group structure from the visual encoding used to visualize its group structure in order to identify interesting gaps in the literature. In so doing, we are able to enumerate a number of lessons learned and gain a better understanding of the outstanding research opportunities and practical implications across domains.", "authors_et_al": false, "substitute": null, "main_image": null, "sync_repositum_override": "projects", "repositum_presentation_id": null, "authors": [ 1850, 1848, 1464, 1410 ], "booktitle": "Proceedings of the 19th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications - Volume 1, HUCAPP and IVAPP", "date_from": "2024-02-27", "date_to": "2024-02-29", "doi": "10.5220/0012431200003660", "event": "19th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications", "isbn": "978-989-758-679-8", "lecturer": [ 1850 ], "location": "Rom", "pages": "12", "pages_from": "697", "pages_to": "708", "research_areas": [ "InfoVis", "NetVis" ], "keywords": [ "compound graph visualization", "literature survey", "group structure visualization" ], "weblinks": [], "files": [], "projects_workgroups": [ "vis" ], "url": "https://www.cg.tuwien.ac.at/research/publications/2024/ehlers-2024-vgs/", "__class": "Publication" }, { "id": "ehlers-2023-iro", "type_id": "journalpaper_notalk", "tu_id": null, "repositum_id": "20.500.12708/192189", "title": "Improving readability of static, straight-line graph drawings: A first look at edge crossing resolution through iterative vertex splitting", "date": "2023-11", "abstract": "We present a novel vertex-splitting approach to iteratively resolve edge crossings in order to improve the readability of graph drawings. Dense graphs, even when small in size (10 to 15 nodes in size) quickly become difficult to read with increasing numbers of edges, and form so-called “hairballs”. The readability of a graph drawing is measured using many different quantitative aesthetic metrics. One such metric of particular importance is the number of edge crossings. Classical approaches to improving readability, such as the minimization of the number of edge crossings, focus on providing overviews of the input graph by aggregating or sampling vertices and/or edges. However, this simplification of the graph drawing does not allow for detailed views into the data, as not all vertices or edges are rendered, and also requires sophisticated interaction approaches to perform well. To avoid this, our locally optimal vertex splitting approach aims to minimize the number of remaining edge crossings while also minimizing the number of vertices that need to be split. In each iteration, we identify the vertex contributing the largest number of edge crossings, remove it, locate the embedding locations of said vertex's two split copies, and determine each copy's unique adjacency. We conduct a user study with 52 participants to evaluate whether vertex splitting affects users’ abilities to conduct a set of graph analytical tasks on graphs 12 nodes in size. Users were tasked with identifying a vertex's adjacency, determining the shared neighbors of two vertices, and checking the validity of a set of paths. We ultimately conclude that within the context of small, dense graphs, systematic vertex splitting is preferred by participants and even positively impacts user performance, though at the cost of the time taken per task.", "authors_et_al": false, "substitute": null, "main_image": null, "sync_repositum_override": "projects", "repositum_presentation_id": null, "authors": [ 1850, 5308, 1410, 1464 ], "doi": "10.1016/j.cag.2023.09.010", "issn": "1873-7684", "journal": "COMPUTERS & GRAPHICS-UK", "pages": "16", "pages_from": "448", "pages_to": "463", "publisher": "PERGAMON-ELSEVIER SCIENCE LTD", "volume": "116", "research_areas": [ "NetVis" ], "keywords": [ "Edge crossings", "Graph aesthetics", "Network visualization", "Vertex splitting" ], "weblinks": [], "files": [], "projects_workgroups": [ "vis" ], "url": "https://www.cg.tuwien.ac.at/research/publications/2023/ehlers-2023-iro/", "__class": "Publication" }, { "id": "sorger-2021-egonet", "type_id": "journalpaper", "tu_id": 300416, "repositum_id": "20.500.12708/58630", "title": "Egocentric Network Exploration for Immersive Analytics", "date": "2021-10", "abstract": "To exploit the potential of immersive network analytics for engaging and effective exploration, we promote the metaphor of ``egocentrism'', where data depiction and interaction are adapted to the perspective of the user within a 3D network. Egocentrism has the potential to overcome some of the inherent downsides of virtual environments, e.g., visual clutter and cyber-sickness. To investigate the effect of this metaphor on immersive network exploration, we designed and evaluated interfaces of varying degrees of egocentrism. In a user study, we evaluated the effect of these interfaces on visual search tasks, efficiency of network traversal, spatial orientation, as well as cyber-sickness. Results show that a simple egocentric interface considerably improves visual search efficiency and navigation performance, yet does not decrease spatial orientation or increase cyber-sickness. A distorted occlusion-free view of the neighborhood only marginally improves the user's performance. We tie our findings together in an open online tool for egocentric network exploration, providing actionable insights on the benefits of the egocentric network exploration metaphor.", "authors_et_al": false, "substitute": null, "main_image": { "description": null, "filetitle": "teaser", "main_file": false, "use_in_gallery": true, "access": "public", "image_width": 2921, "image_height": 735, "name": "sorger-2021-egonet-teaser.png", "type": "image/png", "size": 2079618, "path": "Publication:sorger-2021-egonet", "url": "https://www.cg.tuwien.ac.at/research/publications/2021/sorger-2021-egonet/sorger-2021-egonet-teaser.png", "thumb_image_sizes": [ 16, 64, 100, 175, 300, 600 ], "thumb_url": "https://www.cg.tuwien.ac.at/research/publications/2021/sorger-2021-egonet/sorger-2021-egonet-teaser:thumb{{size}}.png" }, "sync_repositum_override": null, "repositum_presentation_id": null, "authors": [ 1072, 1705, 1720, 853, 1110 ], "cfp": { "name": "Call for Papers _ Pacific Graphics 2021.pdf", "type": "application/pdf", "error": "0", "size": "246798", "orig_name": "Call for Papers _ Pacific Graphics 2021.pdf", "ext": "pdf" }, "date_from": "2021-10-18", "date_to": "2021-10-21", "doi": "10.1111/cgf.14417", "event": "Pacific Graphics 21", "journal": "Computer Graphics Forum", "lecturer": [ 1072 ], "location": "Wellington, NZ", "open_access": "no", "pages": "12", "pages_from": "241", "pages_to": "252", "publisher": "John Wiley and Sons", "volume": "40", "research_areas": [ "InfoVis", "NetVis", "VR" ], "keywords": [ "Computer Graphics and Computer-Aided Design" ], "weblinks": [ { "href": "https://vis.csh.ac.at/egocentricvr/", "caption": "online egocentric network", "description": "Online tool for egocentric network exploration leveraging insights gathered at the user study", "main_file": 1 } ], "files": [ { "description": null, "filetitle": "teaser", "main_file": false, "use_in_gallery": true, "access": "public", "image_width": 2921, "image_height": 735, "name": "sorger-2021-egonet-teaser.png", "type": "image/png", "size": 2079618, "path": "Publication:sorger-2021-egonet", "url": "https://www.cg.tuwien.ac.at/research/publications/2021/sorger-2021-egonet/sorger-2021-egonet-teaser.png", "thumb_image_sizes": [ 16, 64, 100, 175, 300, 600 ], "thumb_url": "https://www.cg.tuwien.ac.at/research/publications/2021/sorger-2021-egonet/sorger-2021-egonet-teaser:thumb{{size}}.png" }, { "description": null, "filetitle": "the paper", "main_file": true, "use_in_gallery": false, "access": "public", "name": "sorger-2021-egonet-the paper.pdf", "type": "application/pdf", "size": 4211458, "path": "Publication:sorger-2021-egonet", "url": "https://www.cg.tuwien.ac.at/research/publications/2021/sorger-2021-egonet/sorger-2021-egonet-the paper.pdf", "thumb_image_sizes": [ 16, 64, 100, 175, 300, 600 ], "thumb_url": "https://www.cg.tuwien.ac.at/research/publications/2021/sorger-2021-egonet/sorger-2021-egonet-the paper:thumb{{size}}.png" }, { "description": null, "filetitle": "video", "main_file": true, "use_in_gallery": false, "access": "public", "name": "sorger-2021-egonet-video.mp4", "type": "video/mp4", "size": 295211748, "path": "Publication:sorger-2021-egonet", "url": "https://www.cg.tuwien.ac.at/research/publications/2021/sorger-2021-egonet/sorger-2021-egonet-video.mp4", "thumb_image_sizes": [ 16, 64, 100, 175, 300, 600 ], "thumb_url": "https://www.cg.tuwien.ac.at/research/publications/2021/sorger-2021-egonet/sorger-2021-egonet-video:thumb{{size}}.png", "video_mp4": "https://www.cg.tuwien.ac.at/research/publications/2021/sorger-2021-egonet/sorger-2021-egonet-video:video.mp4" } ], "projects_workgroups": [ "vis" ], "url": "https://www.cg.tuwien.ac.at/research/publications/2021/sorger-2021-egonet/", "__class": "Publication" }, { "id": "waldner-2020-tbg", "type_id": "journalpaper_notalk", "tu_id": null, "repositum_id": "20.500.12708/141146", "title": "Interactive exploration of large time-dependent bipartite graphs", "date": "2020-04", "abstract": "Bipartite graphs are typically visualized using linked lists or matrices, but these visualizations neither scale well nor do they convey temporal development. We present a new interactive exploration interface for large, time-dependent bipartite graphs. We use two clustering techniques to build a hierarchical aggregation supporting different exploration strategies. Aggregated nodes and edges are visualized as linked lists with nested time series. We demonstrate two use cases: finding advertising expenses of public authorities following similar temporal patterns and comparing author-keyword co-occurrences across time. Through a user study, we show that linked lists with hierarchical aggregation lead to more insights than without.", "authors_et_al": false, "substitute": null, "main_image": { "description": "Dynamic BicFlows with nested time series visualization per cluster per set.", "filetitle": "teaser", "main_file": false, "use_in_gallery": true, "access": "public", "image_width": 1121, "image_height": 936, "name": "waldner-2020-tbg-teaser.png", "type": "image/png", "size": 250456, "path": "Publication:waldner-2020-tbg", "url": "https://www.cg.tuwien.ac.at/research/publications/2020/waldner-2020-tbg/waldner-2020-tbg-teaser.png", "thumb_image_sizes": [ 16, 64, 100, 175, 300, 600 ], "thumb_url": "https://www.cg.tuwien.ac.at/research/publications/2020/waldner-2020-tbg/waldner-2020-tbg-teaser:thumb{{size}}.png" }, "sync_repositum_override": null, "repositum_presentation_id": null, "authors": [ 1110, 1378, 166 ], "doi": "https://doi.org/10.1016/j.cola.2020.100959", "journal": "Journal of Computer Languages", "volume": "57", "research_areas": [ "InfoVis", "NetVis" ], "keywords": [ "Information visualization", "Bipartite graphs", "Clustering", "Time series data", "Insight-based evaluation" ], "weblinks": [ { "href": "https://www.sciencedirect.com/science/article/pii/S2590118420300198", "caption": "paper", "description": "Open Access article at ScienceDirect", "main_file": 1 } ], "files": [ { "description": "Dynamic BicFlows with nested time series visualization per cluster per set.", "filetitle": "teaser", "main_file": false, "use_in_gallery": true, "access": "public", "image_width": 1121, "image_height": 936, "name": "waldner-2020-tbg-teaser.png", "type": "image/png", "size": 250456, "path": "Publication:waldner-2020-tbg", "url": "https://www.cg.tuwien.ac.at/research/publications/2020/waldner-2020-tbg/waldner-2020-tbg-teaser.png", "thumb_image_sizes": [ 16, 64, 100, 175, 300, 600 ], "thumb_url": "https://www.cg.tuwien.ac.at/research/publications/2020/waldner-2020-tbg/waldner-2020-tbg-teaser:thumb{{size}}.png" } ], "projects_workgroups": [ "deskollage" ], "url": "https://www.cg.tuwien.ac.at/research/publications/2020/waldner-2020-tbg/", "__class": "Publication" }, { "id": "sorger-2019-odn", "type_id": "inproceedings", "tu_id": 284199, "repositum_id": null, "title": "Immersive Analytics of Large Dynamic Networks via Overview and Detail Navigation", "date": "2019-12", "abstract": "Analysis of large dynamic networks is a thriving research field, typically relying on 2D graph representations. The advent of affordable head mounted displays sparked new interest in the potential of 3D visualization for immersive network analytics. Nevertheless, most solutions do not scale well with the number of nodes and edges and rely on conventional fly- or walk-through navigation. In this paper, we present a novel approach for the exploration of large dynamic graphs in virtual reality that interweaves two navigation metaphors: overview exploration and immersive detail analysis. We thereby use the potential of state-of-the-art VR headsets, coupled with a web-based 3D rendering engine that supports heterogeneous input modalities to enable ad-hoc immersive network analytics. We validate our approach through a performance evaluation and a case study with experts analyzing medical data.", "authors_et_al": false, "substitute": null, "main_image": { "description": null, "filetitle": "detail view", "main_file": false, "use_in_gallery": true, "access": "public", "image_width": 1600, "image_height": 900, "name": "sorger-2019-odn-detail view.jpg", "type": "image/jpeg", "size": 203956, "path": "Publication:sorger-2019-odn", "url": "https://www.cg.tuwien.ac.at/research/publications/2019/sorger-2019-odn/sorger-2019-odn-detail view.jpg", "thumb_image_sizes": [ 16, 64, 100, 175, 300, 600 ], "thumb_url": "https://www.cg.tuwien.ac.at/research/publications/2019/sorger-2019-odn/sorger-2019-odn-detail view:thumb{{size}}.png" }, "sync_repositum_override": null, "repositum_presentation_id": null, "authors": [ 1072, 1110, 853, 1705 ], "booktitle": "2nd International Conference on Artificial Intelligence & Virtual Reality", "cfp": { "name": "IEEE AIVR 2019.pdf", "type": "application/pdf", "error": "0", "size": "203068", "orig_name": "IEEE AIVR 2019.pdf", "ext": "pdf" }, "event": "AIVR 2019", "lecturer": [ 1072 ], "location": "San Diego, California, USA", "open_access": "yes", "organization": "IEEE", "pages_from": "144", "pages_to": "151", "research_areas": [ "InfoVis", "NetVis" ], "keywords": [ "Immersive Network Analytics", "Web-Based Visualization", "Dynamic Graph Visualization" ], "weblinks": [ { "href": "https://arxiv.org/pdf/1910.06825.pdf", "caption": "arxiv preprint", "description": null, "main_file": 1 }, { "href": "http://aivr.science.uu.nl/", "caption": "IEEE AIVR 2019", "description": null, "main_file": 0 } ], "files": [ { "description": null, "filetitle": "detail view", "main_file": false, "use_in_gallery": true, "access": "public", "image_width": 1600, "image_height": 900, "name": "sorger-2019-odn-detail view.jpg", "type": "image/jpeg", "size": 203956, "path": "Publication:sorger-2019-odn", "url": "https://www.cg.tuwien.ac.at/research/publications/2019/sorger-2019-odn/sorger-2019-odn-detail view.jpg", "thumb_image_sizes": [ 16, 64, 100, 175, 300, 600 ], "thumb_url": "https://www.cg.tuwien.ac.at/research/publications/2019/sorger-2019-odn/sorger-2019-odn-detail view:thumb{{size}}.png" }, { "description": null, "filetitle": "video", "main_file": true, "use_in_gallery": false, "access": "public", "name": "sorger-2019-odn-video.mp4", "type": "video/mp4", "size": 97540271, "path": "Publication:sorger-2019-odn", "url": "https://www.cg.tuwien.ac.at/research/publications/2019/sorger-2019-odn/sorger-2019-odn-video.mp4", "thumb_image_sizes": [ 16, 64, 100, 175, 300, 600 ], "thumb_url": "https://www.cg.tuwien.ac.at/research/publications/2019/sorger-2019-odn/sorger-2019-odn-video:thumb{{size}}.png", "video_mp4": "https://www.cg.tuwien.ac.at/research/publications/2019/sorger-2019-odn/sorger-2019-odn-video:video.mp4" } ], "projects_workgroups": [ "vis" ], "url": "https://www.cg.tuwien.ac.at/research/publications/2019/sorger-2019-odn/", "__class": "Publication" }, { "id": "steinboeck-2018-lbg", "type_id": "inproceedings", "tu_id": null, "repositum_id": null, "title": "Casual Visual Exploration of Large Bipartite Graphs Using Hierarchical Aggregation and Filtering", "date": "2018-10", "abstract": "Bipartite graphs are typically visualized using linked\nlists or matrices. However, these classic visualization techniques\ndo not scale well with the number of nodes. Biclustering has\nbeen used to aggregate edges, but not to create linked lists\nwith thousands of nodes. In this paper, we present a new\ncasual exploration interface for large, weighted bipartite graphs,\nwhich allows for multi-scale exploration through hierarchical\naggregation of nodes and edges using biclustering in linked\nlists. We demonstrate the usefulness of the technique using two\ndata sets: a database of media advertising expenses of public\nauthorities and author-keyword co-occurrences from the IEEE\nVisualization Publication collection. Through an insight-based\nstudy with lay users, we show that the biclustering interface leads\nto longer exploration times, more insights, and more unexpected\nfindings than a baseline interface using only filtering. However,\nusers also perceive the biclustering interface as more complex.", "authors_et_al": false, "substitute": null, "main_image": { "description": "BiCFlows showing visualization authors and their key words", "filetitle": "teaser", "main_file": false, "use_in_gallery": true, "access": "public", "image_width": 708, "image_height": 702, "name": "steinboeck-2018-lbg-teaser.png", "type": "image/png", "size": 137906, "path": "Publication:steinboeck-2018-lbg", "url": "https://www.cg.tuwien.ac.at/research/publications/2018/steinboeck-2018-lbg/steinboeck-2018-lbg-teaser.png", "thumb_image_sizes": [ 16, 64, 100, 175, 300, 600 ], "thumb_url": "https://www.cg.tuwien.ac.at/research/publications/2018/steinboeck-2018-lbg/steinboeck-2018-lbg-teaser:thumb{{size}}.png" }, "sync_repositum_override": null, "repositum_presentation_id": null, "authors": [ 1378, 166, 1110 ], "booktitle": "International Symposium on Big Data Visual and Immersive Analytics", "event": "4th International Symposium on Big Data Visual and Immersive Analytics", "lecturer": [ 1110 ], "location": "Konstanz, Germany", "organization": "IEEE", "research_areas": [ "InfoVis", "NetVis" ], "keywords": [ "information visualization", "bipartite graphs", "biclustering", "insight-based evaluation" ], "weblinks": [ { "href": "https://users.cg.tuwien.ac.at/~waldner/bicflows/", "caption": "BiCFlows online", "description": "BiCFlows online for exploring Austria's media transparency database and the IEEE Visualization paper authors and their key words. ", "main_file": 0 } ], "files": [ { "description": null, "filetitle": "paper", "main_file": true, "use_in_gallery": false, "access": "public", "name": "steinboeck-2018-lbg-paper.pdf", "type": "application/pdf", "size": 1871535, "path": "Publication:steinboeck-2018-lbg", "url": "https://www.cg.tuwien.ac.at/research/publications/2018/steinboeck-2018-lbg/steinboeck-2018-lbg-paper.pdf", "thumb_image_sizes": [ 16, 64, 100, 175, 300, 600 ], "thumb_url": "https://www.cg.tuwien.ac.at/research/publications/2018/steinboeck-2018-lbg/steinboeck-2018-lbg-paper:thumb{{size}}.png" }, { "description": "BiCFlows showing visualization authors and their key words", "filetitle": "teaser", "main_file": false, "use_in_gallery": true, "access": "public", "image_width": 708, "image_height": 702, "name": "steinboeck-2018-lbg-teaser.png", "type": "image/png", "size": 137906, "path": "Publication:steinboeck-2018-lbg", "url": "https://www.cg.tuwien.ac.at/research/publications/2018/steinboeck-2018-lbg/steinboeck-2018-lbg-teaser.png", "thumb_image_sizes": [ 16, 64, 100, 175, 300, 600 ], "thumb_url": "https://www.cg.tuwien.ac.at/research/publications/2018/steinboeck-2018-lbg/steinboeck-2018-lbg-teaser:thumb{{size}}.png" } ], "projects_workgroups": [ "deskollage" ], "url": "https://www.cg.tuwien.ac.at/research/publications/2018/steinboeck-2018-lbg/", "__class": "Publication" }, { "id": "mazurek-2018-veq", "type_id": "journalpaper", "tu_id": null, "repositum_id": null, "title": "Visualizing Expanded Query Results", "date": "2018-06", "abstract": "When performing queries in web search engines, users often face difficulties choosing appropriate query terms. Search engines\ntherefore usually suggest a list of expanded versions of the user query to disambiguate it or to resolve potential term mismatches.\nHowever, it has been shown that users find it difficult to choose an expanded query from such a list. In this paper, we describe\nthe adoption of set-based text visualization techniques to visualize how query expansions enrich the result space of a given\nuser query and how the result sets relate to each other. Our system uses a linguistic approach to expand queries and topic\nmodeling to extract the most informative terms from the results of these queries. In a user study, we compare a common text list\nof query expansion suggestions to three set-based text visualization techniques adopted for visualizing expanded query results\n– namely, Compact Euler Diagrams, Parallel Tag Clouds, and a List View – to resolve ambiguous queries using interactive\nquery expansion. Our results show that text visualization techniques do not increase retrieval efficiency, precision, or recall.\nOverall, users rate Parallel Tag Clouds visualizing key terms of the expanded query space lowest. Based on the results, we derive\nrecommendations for visualizations of query expansion results, text visualization techniques in general, and discuss alternative\nuse cases of set-based text visualization techniques in the context of web search.", "authors_et_al": false, "substitute": null, "main_image": { "description": "Density-based compact Euler Diagram", "filetitle": "teaser", "main_file": false, "use_in_gallery": true, "access": "public", "image_width": 919, "image_height": 656, "name": "mazurek-2018-veq-teaser.png", "type": "image/png", "size": 34882, "path": "Publication:mazurek-2018-veq", "url": "https://www.cg.tuwien.ac.at/research/publications/2018/mazurek-2018-veq/mazurek-2018-veq-teaser.png", "thumb_image_sizes": [ 16, 64, 100, 175, 300, 600 ], "thumb_url": "https://www.cg.tuwien.ac.at/research/publications/2018/mazurek-2018-veq/mazurek-2018-veq-teaser:thumb{{size}}.png" }, "sync_repositum_override": null, "repositum_presentation_id": null, "authors": [ 1364, 1110 ], "cfp": { "name": "EuroVis 2018 CfP.pdf", "type": "application/pdf", "error": "0", "size": "55844", "orig_name": "EuroVis 2018 CfP.pdf", "ext": "pdf" }, "date_from": "2018-06-04", "date_to": "2018-06-08", "event": "EG / VGTC Conference on Visualization (EuroVis)", "journal": "Computer Graphics Forum", "lecturer": [ 1364 ], "location": "Brno, Czech Republic", "pages_from": "87", "pages_to": "98", "research_areas": [ "InfoVis", "NetVis" ], "keywords": [ "Information visualization", "search interfaces", "empirical studies in visualization" ], "weblinks": [], "files": [ { "description": null, "filetitle": "paper", "main_file": true, "use_in_gallery": false, "access": "public", "name": "mazurek-2018-veq-paper.pdf", "type": "application/pdf", "size": 1891501, "path": "Publication:mazurek-2018-veq", "url": "https://www.cg.tuwien.ac.at/research/publications/2018/mazurek-2018-veq/mazurek-2018-veq-paper.pdf", "thumb_image_sizes": [ 16, 64, 100, 175, 300, 600 ], "thumb_url": "https://www.cg.tuwien.ac.at/research/publications/2018/mazurek-2018-veq/mazurek-2018-veq-paper:thumb{{size}}.png" }, { "description": "Density-based compact Euler Diagram", "filetitle": "teaser", "main_file": false, "use_in_gallery": true, "access": "public", "image_width": 919, "image_height": 656, "name": "mazurek-2018-veq-teaser.png", "type": "image/png", "size": 34882, "path": "Publication:mazurek-2018-veq", "url": "https://www.cg.tuwien.ac.at/research/publications/2018/mazurek-2018-veq/mazurek-2018-veq-teaser.png", "thumb_image_sizes": [ 16, 64, 100, 175, 300, 600 ], "thumb_url": "https://www.cg.tuwien.ac.at/research/publications/2018/mazurek-2018-veq/mazurek-2018-veq-teaser:thumb{{size}}.png" }, { "description": null, "filetitle": "video", "main_file": true, "use_in_gallery": false, "access": "public", "name": "mazurek-2018-veq-video.mp4", "type": "video/mp4", "size": 30231176, "path": "Publication:mazurek-2018-veq", "url": "https://www.cg.tuwien.ac.at/research/publications/2018/mazurek-2018-veq/mazurek-2018-veq-video.mp4", "thumb_image_sizes": [ 16, 64, 100, 175, 300, 600 ], "thumb_url": "https://www.cg.tuwien.ac.at/research/publications/2018/mazurek-2018-veq/mazurek-2018-veq-video:thumb{{size}}.png", "video_mp4": "https://www.cg.tuwien.ac.at/research/publications/2018/mazurek-2018-veq/mazurek-2018-veq-video:video.mp4" } ], "projects_workgroups": [ "deskollage" ], "url": "https://www.cg.tuwien.ac.at/research/publications/2018/mazurek-2018-veq/", "__class": "Publication" }, { "id": "waldner-2014-ghi", "type_id": "inproceedings", "tu_id": null, "repositum_id": null, "title": "Graphical Histories of Information Foraging", "date": "2014-10", "abstract": "During information foraging, knowledge workers iteratively seek, filter, read, and extract information. When using multiple information sources and different applications for information processing, re-examination of activities for validation of previous decisions or re-discovery of previously used information sources is challenging. In this paper, we present a novel representation of cross-application histories to support recall of past operations and re-discovery of information resources. Our graphical history consists of a cross-scale visualization combining an overview node-link diagram of used desktop resources with nested (animated) snapshot sequences, based on a recording of the visual screen output during the users’ desktop work. This representation makes key elements of the users’ tasks visually stand out, while exploiting the power of visual memory to recover subtle details of their activities. In a preliminary study, users found our graphical history helpful to recall details of an information foraging task and commented positively on the ability to expand overview nodes into snapshot and video sequences.", "authors_et_al": false, "substitute": null, "main_image": { "description": null, "filetitle": "", "main_file": false, "use_in_gallery": false, "access": "public", "image_width": 748, "image_height": 668, "name": "waldner-2014-ghi-.png", "type": "image/png", "size": 118153, "path": "Publication:waldner-2014-ghi", "url": "https://www.cg.tuwien.ac.at/research/publications/2014/waldner-2014-ghi/waldner-2014-ghi-.png", "thumb_image_sizes": [ 16, 64, 100, 175, 300, 600 ], "thumb_url": "https://www.cg.tuwien.ac.at/research/publications/2014/waldner-2014-ghi/waldner-2014-ghi-:thumb{{size}}.png" }, "sync_repositum_override": null, "repositum_presentation_id": null, "authors": [ 1110, 161, 171 ], "booktitle": "Proceedings of the 8th Nordic Conference on Human-Computer Interaction: Fun, Fast, Foundational ", "date_from": "2014-10-26", "date_to": "2014-10-30", "isbn": "978-1-4503-2542-4", "lecturer": [ 1110 ], "location": "Helsinki, Finland", "organization": "NordiCHI’14 - Nordic Conference on Human-Computer Interaction", "pages_from": "295", "pages_to": "304", "publisher": "ACM", "research_areas": [ "InfoVis", "NetVis" ], "keywords": [ "Graph visualization", "Interaction history", "Provenance" ], "weblinks": [], "files": [ { "description": null, "filetitle": "", "main_file": false, "use_in_gallery": false, "access": "public", "image_width": 748, "image_height": 668, "name": "waldner-2014-ghi-.png", "type": "image/png", "size": 118153, "path": "Publication:waldner-2014-ghi", "url": "https://www.cg.tuwien.ac.at/research/publications/2014/waldner-2014-ghi/waldner-2014-ghi-.png", "thumb_image_sizes": [ 16, 64, 100, 175, 300, 600 ], "thumb_url": "https://www.cg.tuwien.ac.at/research/publications/2014/waldner-2014-ghi/waldner-2014-ghi-:thumb{{size}}.png" }, { "description": null, "filetitle": "paper", "main_file": true, "use_in_gallery": false, "access": "public", "name": "waldner-2014-ghi-paper.pdf", "type": "application/pdf", "size": 3116802, "path": "Publication:waldner-2014-ghi", "url": "https://www.cg.tuwien.ac.at/research/publications/2014/waldner-2014-ghi/waldner-2014-ghi-paper.pdf", "thumb_image_sizes": [ 16, 64, 100, 175, 300, 600 ], "thumb_url": "https://www.cg.tuwien.ac.at/research/publications/2014/waldner-2014-ghi/waldner-2014-ghi-paper:thumb{{size}}.png" } ], "projects_workgroups": [ "illvisation" ], "url": "https://www.cg.tuwien.ac.at/research/publications/2014/waldner-2014-ghi/", "__class": "Publication" } ]