DKGV: A Dynamic Knowledge Graph Visualization Method Based on Force-Directed Layout
Authors: 
Hong Zhou, Sunjie Huang, Yushan Zheng, Jinyan Liu, Yang Chen, Xiangyuan Chen, Jun LiAuthors Info & Claims
Hong Zhou, Sunjie Huang, Yushan Zheng, Jinyan Liu, Yang Chen, Xiangyuan Chen, Jun LiAuthors Info & ClaimsArticle No.: 3, Pages 1 - 8
Abstract
The dynamic knowledge graph is a data structure that adds temporal information to the nodes and edges of a traditional knowledge graph. It describes the changing processes of entities and relationships over time, thereby enriching and updating the expression of knowledge. The dynamic knowledge graph possesses characteristics such as time and labels; it is not only a knowledge graph but also a type of dynamic graph containing heterogeneous information.
To address the issue that traditional dynamic graph visualization methods do not account for heterogeneous information in the layout, leading to a relatively random distribution of node labels in the visualization results, this paper proposes DKGV, a dynamic knowledge graph visualization method based on a force-directed layout. DKGV utilizes an improved GCN to generate the initial layout of the dynamic knowledge graph data, performs static force-directed layout iterative processing, and conducts dynamic force-directed layout calculations on the dynamic knowledge graph. Finally, it employs a force-directed layout with boundaries to support three-dimensional temporal display after view transformation.
Experimental results show that DKGV can maintain the stability of the node layout during the evolution of the dynamic knowledge graph while making the overall layout more regular to better display the relationships between entities.
References
[1]
Arleo Alessio, Miksch Silvia, and W. Archambault Daniel. 2021. A Multilevel Approach for Event-Based Dynamic Graph Drawing. In Eurographics Conference on Visualization. https://api.semanticscholar.org/CorpusID:237248310
[2]
Benjamin Bach, Emmanuel Pietriga, Ilaria Liccardi, and Gennady Legostaev. 2011. OntoTrix: a hybrid visualization for populated ontologies. In Proceedings of the 20th International Conference Companion on World Wide Web (Hyderabad, India) (WWW ’11). Association for Computing Machinery, New York, NY, USA, 177–180. https://doi.org/10.1145/1963192.1963283
[3]
Kale Bharati, Sun Maoyuan, and Papka Michael E.2023. The State of the Art in Visualizing Dynamic Multivariate Networks. Computer Graphics Forum 42 (2023). https://api.semanticscholar.org/CorpusID:259263777
[4]
Limei Che, Jie Liang, Xiaoru Yuan, Jianping Shen, Jinquan Xu, and Yong Li. 2015. Laplacian-based dynamic graph visualization. In 2015 IEEE Pacific Visualization Symposium (PacificVis). 69–73. https://doi.org/10.1109/PACIFICVIS.2015.7156358
[5]
D3. [n. d.]. https://observablehq.com/@d3/force-directed-graph-component.
[6]
Timo Eloranta and Erkki Mäkinen. 2001. TimGA: A genetic algorithm for drawing undirected graphs.Divulgaciones Matemáticas 9, 2 (2001), 155–170. http://eudml.org/doc/49845
[7]
Baehrecke Eric H., Dang Niem, Babaria Ketan, and Shneiderman Ben. 2004. Visualization and analysis of microarray and gene ontology data with treemaps. BMC Bioinformatics 5 (2004), 84 – 84. https://api.semanticscholar.org/CorpusID:7186140
[8]
Bahar Ghadiri Bashardoost, Christina Christodoulakis, Soheil Hassas Yeganeh, Renée J. Miller, Kelly Lyons, and Oktie Hassanzadeh. 2015. VizCurator: A Visual Tool for Curating Open Data. In Proceedings of the 24th International Conference on World Wide Web (Florence, Italy) (WWW ’15 Companion). Association for Computing Machinery, New York, NY, USA, 195–198. https://doi.org/10.1145/2740908.2742845
[9]
Tilman Göhnert, Sabrina Ziebarth, Henrik Detjen, Tobias Hecking, and H. Ulrich Hoppe. 2015. 3D DynNetVis: A 3D Visualization Technique for Dynamic Networks. In Proceedings of the 2015 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2015 (Paris, France) (ASONAM ’15). Association for Computing Machinery, New York, NY, USA, 737–740. https://doi.org/10.1145/2808797.2808798
[10]
Thomas E. Gorochowski, Mario di Bernardo, and Claire S. Grierson. 2012. Using Aging to Visually Uncover Evolutionary Processes on Networks. IEEE Transactions on Visualization and Computer Graphics 18, 8 (2012), 1343–1352. https://doi.org/10.1109/TVCG.2011.142
[11]
William L. Hamilton, Rex Ying, and Jure Leskovec. 2017. Inductive representation learning on large graphs. In Proceedings of the 31st International Conference on Neural Information Processing Systems (Long Beach, California, USA) (NIPS’17). Curran Associates Inc., Red Hook, NY, USA, 1025–1035.
[12]
Nathalie Henry, Jean-Daniel Fekete, and Michael J. McGuffin. 2007. NodeTrix: a Hybrid Visualization of Social Networks. IEEE Transactions on Visualization and Computer Graphics 13, 6 (2007), 1302–1309. https://doi.org/10.1109/TVCG.2007.70582
[13]
Masahiko Itoh and Mina Akaishi. 2012. Visualization for Changes in Relationships between Historical Figures in Chronicles. In 2012 16th International Conference on Information Visualisation. 283–290. https://doi.org/10.1109/IV.2012.55
[14]
Kang Jianzi and Zhou Hong. 2024. Force-Directed Layout Based Knowledge Graph Visualization Method. Journal of Computer-Aided Design & Computer Graphics 36, 8 (2024), 1214–1223. https://doi.org/10.3724/SP.J.1089.2024.19955
[15]
McInnes Leland and Healy John. 2018. UMAP: Uniform Manifold Approximation and Projection for Dimension Reduction. ArXiv abs/1802.03426 (2018). https://api.semanticscholar.org/CorpusID:3641284
[16]
Y Liu, ZJ Fu, J Li, and L Hou. 2018. Generation of medical encyclopedia knowledge graph. Chin J Med Libr Inf Sci Internet (2018).
[17]
Zipeng Liu, Yang Wang, Jürgen Bernard, and Tamara Munzner. 2022. Visualizing Graph Neural Networks With CorGIE: Corresponding a Graph to Its Embedding. IEEE Transactions on Visualization and Computer Graphics 28, 6 (2022), 2500–2516. https://doi.org/10.1109/TVCG.2022.3148197
[18]
Bastian Mathieu, Heymann Sebastien, and Jacomy Mathieu. 2009. Gephi: An Open Source Software for Exploring and Manipulating Networks. Proceedings of the International AAAI Conference on Web and Social Media (2009). https://api.semanticscholar.org/CorpusID:15996046
[19]
Jacomy Mathieu, Venturini Tommaso, Heymann Sebastien, and Bastian Mathieu. 2014. ForceAtlas2, a Continuous Graph Layout Algorithm for Handy Network Visualization Designed for the Gephi Software. PLoS ONE 9 (2014). https://api.semanticscholar.org/CorpusID:14763635
[20]
Natalya Fridman Noy, Ray W. Fergerson, and Mark A. Musen. 2000. The Knowledge Model of Protégé-2000: Combining Interoperability and Flexibility. In Knowledge Engineering and Knowledge Management Methods, Models, and Tools, Rose Dieng and Olivier Corby (Eds.). Springer Berlin Heidelberg, Berlin, Heidelberg, 17–32.
[21]
Lei Shi, Chen Wang, and Zhen Wen. 2011. Dynamic network visualization in 1.5D. In 2011 IEEE Pacific Visualization Symposium. 179–186. https://doi.org/10.1109/PACIFICVIS.2011.5742388
[22]
Paolo Simonetto, Daniel Archambault, and Stephen Kobourov. 2020. Event-Based Dynamic Graph Visualisation. IEEE Transactions on Visualization and Computer Graphics 26, 7 (2020), 2373–2386. https://doi.org/10.1109/TVCG.2018.2886901
[23]
Jian Tang, Meng Qu, Mingzhe Wang, Ming Zhang, Jun Yan, and Qiaozhu Mei. 2015. LINE: Large-scale Information Network Embedding. In Proceedings of the 24th International Conference on World Wide Web (Florence, Italy) (WWW ’15). International World Wide Web Conferences Steering Committee, Republic and Canton of Geneva, CHE, 1067–1077. https://doi.org/10.1145/2736277.2741093
[24]
Kipf Thomas and Welling Max. 2016. Semi-Supervised Classification with Graph Convolutional Networks. ArXiv abs/1609.02907 (2016). https://api.semanticscholar.org/CorpusID:3144218
[25]
Tushare. [n. d.]. https://tushare.pro/.
[26]
Aleksa Vukotic, Nicki Watt, Tareq Abedrabbo, Dominic Fox, and Jonas Partner. 2015. Neo4j in Action. Manning. https://www.amazon.com/Neo4j-Action-Aleksa-Vukotic/dp/1617290769
[27]
Taowei David Wang and Bijan Parsia. 2006. CropCircles: Topology Sensitive Visualization of OWL Class Hierarchies. In The Semantic Web - ISWC 2006, Isabel Cruz, Stefan Decker, Dean Allemang, Chris Preist, Daniel Schwabe, Peter Mika, Mike Uschold, and Lora M. Aroyo (Eds.). Springer Berlin Heidelberg, Berlin, Heidelberg, 695–708.
Index Terms
- DKGV: A Dynamic Knowledge Graph Visualization Method Based on Force-Directed Layout
Recommendations
Integrating edge routing into force-directed layout
GD'06: Proceedings of the 14th international conference on Graph drawingThe typical use of force-directed layout is to create organic-looking, straight-edge drawings of large graphs while combinatorial techniques are generally preferred for high-quality layout of small to medium sized graphs. In this paper we integrate edge-...
A regularized graph layout framework for dynamic network visualization
Many real-world networks, including social and information networks, are dynamic structures that evolve over time. Such dynamic networks are typically visualized using a sequence of static graph layouts. In addition to providing a visual representation ...
Distributed force-directed graph layout and visualization
EGPGV '06: Proceedings of the 6th Eurographics conference on Parallel Graphics and VisualizationWhile there exist many interactive tools for the visualization of small graphs and networks, these tools do not address the fundamental problems associated with the visualization of large graphs. In particular, larger graphs require much larger display ...
Comments
Information & Contributors
Information
Published In
December 2024
161 pages
ISBN:9798400713484
DOI:10.1145/3703619
- Editors:
- Ping Li,
- Zhigeng Pan,
- Adrian David Cheok,
- Lei Zhu,
- Zhihua Hu
Copyright © 2024 ACM.
Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than the author(s) must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected].
Sponsors
Publisher
Association for Computing Machinery
New York, NY, United States
Publication History
Published: 19 January 2025
Check for updates
Author Tags
Qualifiers
- Research-article
- Research
- Refereed limited
Funding Sources
- Shenzhen Science and Technology Major Project
Conference
VRCAI '24
Sponsor:
VRCAI '24: The 19th ACM SIGGRAPH International Conference on Virtual-Reality Continuum and its Applications in Industry
December 1 - 2, 2024
Guangdong Province, Nanjing, China
Acceptance Rates
Overall Acceptance Rate 51 of 107 submissions, 48%
Contributors
Other Metrics
Bibliometrics & Citations
Bibliometrics
Article Metrics
- 0Total Citations
- 57Total Downloads
- Downloads (Last 12 months)57
- Downloads (Last 6 weeks)46
Reflects downloads up to 08 Mar 2025
Other Metrics
Citations
View Options
Login options
Check if you have access through your login credentials or your institution to get full access on this article.
Sign inFull Access
View options
View or Download as a PDF file.
PDFeReader
View online with eReader.
eReaderHTML Format
View this article in HTML Format.
HTML Format