Abstract
Exploring relationships in complex datasets is one of the challenges in today’s big data era. The graph-based visualization approach, which integrates the advantages of graph analysis theory and visualization technologies and combines machine and human intelligence, has become an effective means for analyzing various relationships in complex datasets. In this paper, we first introduce a graph-based visual analytics model for associated data. Then, we summarize seven typical visualization methods for associated data according to their layout features, including their node-link diagram, adjacency matrix, hypergraph, flow diagram, graphs with geospatial information, multi-attribute graph, and space-filling diagram and discuss their advantages and disadvantages. We describe current graph simplification and interaction techniques, including graph filtering, node clustering, edge bundling, graph data dimension reduction, and topology-based graph transformation. Finally, we discuss the potential challenges and developmental trends of the research direction.
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Abdelsadek Y, Chelghoum K, Herrmann F et al (2018) Community extraction and visualization in social networks applied to Twitter. Inf Sci 424:204–223. https://doi.org/10.1016/j.ins.2017.09.022
Aggarwal CC, Wang H (2011) On dimensionality reduction of massive graphs for indexing and retrieval. In: IEEE, international conference on data engineering. IEEE Computer Society, pp 1091–1102. https://doi.org/10.1109/ICDE.2011.5767834
Ahn YY, Han S, Kwak H et al (2007) Analysis of topological characteristics of huge online social networking services. In: International conference on World Wide Web. ACM. https://doi.org/10.1145/1242572.1242685
Al-Awami AK, Beyer J, Strobelt H et al (2014) Neurolines: a subway map metaphor for visualizing nanoscale neuronal connectivity. IEEE Trans Vis Comput Graph 20(12):2369–2378. https://doi.org/10.1109/TVCG.2014.234631
Amar RA, Eagan J, Stasko JT (2005) Low level components of analytic activity in information visualization. In: Proceedings of the IEEE conference on information visualization, pp 111–117. http://doi.org/10.1109/INFVIS.2005.1532136
Balzer M, Deussen O, Lewerentz C (2005) Voronoi treemaps for the visualization of software metrics. In: Softvis. ACM, pp 165–172. https://doi.org/10.1145/1056018.1056041
Beck F, Wiszniewsky FJ, Burch M et al (2014b) Asymmetric visual hierarchy comparison with nested Icicle plots. In: Joint proceedings of the 4th international workshop Euler diagrams 1st international workshop graph visualization practice, pp 53–62
Beck F, Burch M, Diehl S et al (2017) A taxonomy and survey of dynamic graph visualization. Comput Graph Forum. https://doi.org/10.1111/cgf.12791
Bezerianos A, Chevalier F, Dragicevic P et al (2010) Graphdice: a system for exploring multivariate social networks. Comput Graph Forum 29(3):863–872. https://doi.org/10.1111/j.1467-8659.2009.01687.x
Boutin F, Thièvre J, Hascoët M (2005) Multilevel compound tree–construction visualization and interaction. In: Proceedings of interact 2005. Lecture notes in computer science, LNCS 3585, Springer, pp 847–860. https://doi.org/10.1007/11555261_67
Brandes U, Pich C (2008) An experimental study on distance-based graph drawing. In: Proceedings of international symposium on graph drawing. Springer, Berlin, pp 218–229. https://doi.org/10.1007/978-3-642-00219-9_21
Bunke H, Foggia P, Guidobaldi C et al (2002) A comparison of algorithms for maximum common subgraph on randomly connected graphs. In: Structural, syntactic, and statistical pattern recognition, pp 85–106. https://doi.org/10.1007/3-540-70659-3_12
Cao N, Lin Y R, Li L et al (2015) g-Miner: interactive visual group mining on multivariate graphs. In: Proceedings of the 33rd annual ACM conference on human factors in computing systems. ACM Press, New York, pp 279–288. https://doi.org/10.1145/2702123.2702446
Chang C, Bach B, Dwyer T et al (2017) Evaluating perceptually complementary views for network exploration tasks. In: Proceedings of the 2017 CHI conference on human factors in computing systems. ACM Press, New York, pp 1397–1407. https://doi.org/10.1145/3025453.3026024
Chen Y, Hu H, Li Z et al (2013) Performance compare and optimization of rectangular treemap layout algorithms. J Comput Aided Des Comput Graph 25(11):1623–1634. https://doi.org/10.3969/j.issn.1003-9775.2013.11.004
Chen Y, Dong Y, Sun YH, Liang J (2018) A multi-comparable visual analytic approach for complex hierarchical data. J Vis Lang Comput 47:19–30. https://doi.org/10.1016/j.jvlc.2018.02.003
Cordella LP, Foggia P, Sansone C et al (2004) A (sub) graph isomorphism algorithm for matching large graphs. IEEE Trans Pattern Anal Mach Intell 26(10):1367–1372. https://doi.org/10.1109/TPAMI.2004.75
Du X, Chen Y, Li Y (2018) TransGraph: a transform-based graph for analyzing relations in data set. J Comput Aided Des Comput Graph 30(1):79–89. https://doi.org/10.3724/SP.J.1089.2018.16920
Dunne C, Shneiderman B (2013) Motif simplification: improving network visualization readability with fan, connector, and clique glyphs. In: SIGCHI conference on human factors in computing systems. ACM. https://doi.org/10.1145/2470654.2466444
Ellis G, Dix A (2007) A taxonomy of clutter reduction for information visualisation. IEEE Trans Vis Comput Graph 13(6):1216–1223. https://doi.org/10.1109/TVCG.2007.70535
Fischer F, Fuchs J, Mansmann F (2012) ClockMap: enhancing circular treemaps with temporal glyphs for time-series data. In: Proceedings of Eurographics conference on visualization. Eurographics Association Press, Aire-La-Ville, pp 97–101
Fortunato S (2009) Community detection in graphs. Phys Rep 486(3):75–174. https://doi.org/10.1016/j.physrep.2009.11.002
Fruchterman TMJ, Reingold EM (1991) Graph drawing by force-directed placement. Softw Pract Exp 21(11):1129–1164. https://doi.org/10.1002/spe.4380211102
Gao L, Yang J, Qin G (2013) Methods for pattern mining in dynamic networks and applications. J Softw 24(9):2042–2061. https://doi.org/10.3724/SP.J.1001.2013.04439
Ghoniem M, Fekete JD, Castagliola P (2005) A comparison of the readability of graphs using node-link and matrix-based representations. In: Proceedings of IEEE symposium on information visualization. IEEE Computer Society Press, Washington, DC, pp 17–24. https://doi.org/10.1109/INFVIS.2004.1
Graham M, Kennedy J (2010) A survey of multiple tree visualisation. Inf Vis 9(4):235–252. https://doi.org/10.1057/ivs.2009.29
Grover A, Leskovec J (2016) node2vec: scalable feature learning for networks. In: ACM SIGKDD international conference on knowledge discovery and data mining. ACM, pp 855–864. https://doi.org/10.1145/2939672.2939754
Hadlak S, Schumann H, Schulz HJ (2015) A survey of multi-faceted graph visualization. EuroVis’15 state-of-the-art report. https://doi.org/10.2312/eurovisstar.20151109
Hagmann P, Cammoun L et al (2008) Mapping the structural core of human cerebral cortex. PLoS Biol 6(7):e159
Heer J, Bostock M, Ogievetsky V (2010) A tour through the visualization zoo. Commun ACM 53(6):59–67. https://doi.org/10.1145/1743546.1743567
Henry N, Fekete JD (2007) Matlink: enhanced matrix visualization for analyzing social networks. In: Human–computer interaction—INTERACT, pp 288–302. https://doi.org/10.1007/978-3-540-74800-7_24
Henry N, Fekete JD, McGuffin MJ (2007) NodeTrix: a hybrid visualization of social networks. IEEE Trans Vis Comput Graph 13(6):1302–1309. https://doi.org/10.1109/TVCG.2007.70582
Holten D (2006) Hierarchical edge bundles: visualization of adjacency relations in hierarchical data. IEEE Trans Vis Comput Graph 12(5):741–748. https://doi.org/10.1109/TVCG.2006.147
Holten D, Van Wijk JJ (2009) Force-directed edge bundling for graph visualization. Comput Graph Forum 28(3):983–990. https://doi.org/10.1111/j.1467-8659.2009.01450.x
Hu H, Chen Y, Zhen Y et al (2014) A squarified and ordered treemap layout algorithm. J Comput Aided Des Comput Graph 26(10):1703–1710. https://doi.org/10.3969/j.issn.1003-9775.2014.10.018
Huang X, Lai W (2006) Clustering graphs for visualization via node similarities. J Vis Lang Comput 17(3):225–253. https://doi.org/10.1016/j.jvlc.2005.10.003
Itoh T, Klein K (2015) Key-node-separated graph clustering and layouts for human relationship graph visualization. IEEE Comput Graph Appl 35(6):30–40. https://doi.org/10.1109/MCG.2015.115
Iturbe M, Garitano I, Zurutuza U et al (2016) Visualizing network flows and related anomalies in industrial networks using chord diagrams and whitelisting. In: Proceedings of the 11th international joint conference on computer vision, imaging and computer graphics theory and applications, vol 2. VISIGRAPP, Rome, pp 99–106. https://doi.org/10.5220/0005670000990106
Jarukasemratana S, Murata T (2013) Recent large graph visualization tools: a review. IMT 8:944–960. https://doi.org/10.11185/imt.8.944
Jiang Y, Jia C, Yu J (2011) Community detection in complex networks based on vertex similarities. Comput Sci 38(7):185–189. https://doi.org/10.3969/j.issn.1002-137X.2011.07.041
Keim D, Andrienko G, Fekete JD et al (2008) Visual analytics: definition, process, and challenges. In: Proceedings of the information visualization. Springer, Berlin, pp 154–175. https://doi.org/10.1007/978-3-540-70956-5_7
Kerracher N, Kennedy J, Chalmers K (2015) A task taxonomy for temporal graph visualisation. IEEE Trans Vis Comput Graph 21(10):1160–1172. https://doi.org/10.1109/TVCG.2015.2424889
Kerren A, Jusufi I (2013) A novel radial visualization approach for undirected hypergraphs. In: Proceedings of 17th Eurographics conference on visualization. Eurographics Association Press, Aire-La-Ville, pp 25–29
Krüger R, Simeonov G, Beck F et al (2018) Visual interactive map matching. IEEE Trans Vis Comput Graph 24(6):1881–1892. https://doi.org/10.1109/TVCG.2018.2816219
Kwon OH, Muelder C, Lee K et al (2015) Spherical layout and rendering methods for immersive graph visualization. In: Proceedings of the 2015 IEEE pacific visualization symposium. IEEE Press, Piscataway, NJ, pp 63–67. https://doi.org/10.1109/PACIFICVIS.2015.7156357
Kwon OH, Muelder C, Lee K et al (2016) A study of layout, rendering, and interaction methods for immersive graph visualization. IEEE Trans Vis Comput Graph 22(7):1802–1815. https://doi.org/10.1109/TVCG.2016.2520921
Kwon OH, Crnovrsanin T, Ma KL (2017) What would a graph look like in this layout? A machine learning approach to large graph visualization. IEEE Trans Vis Comput Graph 24(1):477–488. https://doi.org/10.1109/TVCG.2017.2743858
Lee B, Plaisant C, Parr CS et al (2006) Task taxonomy for graph visualization. In: Proceedings of AVI workshop on beyond time and errors: novel evaluation methods for information visualization. ACM Press, New York, pp 1–5. https://doi.org/10.1145/1168149.1168168
Lhuillier A, Hurter C, Telea A (2017) State of the art in edge and trail bundling techniques. Comput Graph Forum 36(3):619–645. https://doi.org/10.1111/cgf.13213
Li X, Li J, Gao H (2007) An efficient frequent subgraph mining algorithm. J Softw 18(10):2469–2480. https://doi.org/10.1360/jos182469
Liu S, Cui W, Wu Y et al (2014) A survey on information visualization: recent advances and challenges. Vis Comput Int J Comput Graph 30(12):1373–1393. https://doi.org/10.1007/s00371-013-0892-3
Liu M, Liu S, Zhu X et al (2016a) An uncertainty-aware approach for exploratory microblog retrieval. IEEE Trans Vis Comput Graph 22(1):250–259. https://doi.org/10.1109/TVCG.2015.2467554
Liu M, Shi J, Li Z et al (2016b) Towards better analysis of deep convolutional neural networks. IEEE Trans Vis Comput Graph 23(1):91–100. https://doi.org/10.1109/TVCG.2016.2598831
Lupton RC, Allwood JM (2017) Hybrid Sankey diagrams: visual analysis of multidimensional data for understanding resource use. Resour Conserv Recycl 124:141–151. https://doi.org/10.1016/j.resconrec.2017.05.002
Melancon G (2006) Just how dense are dense graphs in the real world? A methodological note. In: Proceedings of the 2006 AVI workshop on beyond time and errors: novel evaluation methods for information visualization. ACM Press, New York, pp 1–7. https://doi.org/10.1145/1168149.1168167
Neumann P, Schlechtweg S, Carpendale S (2005) ArcTrees: visualizing relations in hierarchical data. In: Proceedings of Eurographics conference on visualization. Eurographics Association Press, Aire-La-Ville, pp 53–60. https://doi.org/10.2312/VisSym/EuroVis05/053-060
Papadopoulos S, Kompatsiaris Y, Vakali A et al (2012) Community detection in social media. Data Min Knowl Disc 24(3):515–554. https://doi.org/10.1007/s10618-011-0224-z
Pienta R, Hohman F, Endert A et al (2017) VIGOR: interactive visual exploration of graph query results. IEEE Trans Vis Comput Graph 24(1):215–225. https://doi.org/10.1109/TVCG.2017.2744898
Pretorius AJ, Purchase HC, Stasko JT (2014) Tasks for multivariate network analysis. Lecture notes in computer science (including subseries lecture notes in artificial intelligence and lecture notes in bioinformatics). Dagstuhl Seminar, 12–17 May 2013, Dagstuhl Castle, Germany. Springer, pp 77–95. https://doi.org/10.1007/978-3-319-06793-35
Ren L, Du Y, Ma S (2014) Visual analytics towards big data. J Softw 9:1909–1936. https://doi.org/10.13328/j.cnki.jos.004645
Riegler M, Pogorelov K, Lux M et al (2016) Explorative hyperbolic-tree-based clustering tool for unsupervised knowledge discovery. In: Conference: conference: international workshop on content-based multimedia indexing 2016, pp 1–4. https://doi.org/10.1109/CBMI.2016.7500271
Sallaberry A, Fu Y, Ho HC et al (2016) Contact trees: network visualization beyond nodes and edges. PLoS ONE 11(1):1–23. https://doi.org/10.1371/journal.pone.0146368
Santos JM, Dias P, Santos BS (2012) Implementation and evaluation of an enhanced h-tree layout pedigree visualization. In: Proceedings of 2012 16th international conference on information visualisation. IEEE Computer Society Press, Washington, DC, pp 24–29. https://doi.org/10.1109/IV.2012.15
Sarkar M, Brown MH (1992) Graphical fisheye views of graphs. In: Proceedings of the ACM conference on human factors in computing systems. ACM, pp 83–91. https://doi.org/10.1145/142750.142763
Schöffel S, Schwank J, Stärz J et al (2016) Multivariate networks: a novel edge visualization approach for graph-based visual analysis tasks. In: Proceedings of the 2016 CHI conference extended abstracts on human factors in computing systems. ACM Press, New York, pp 2292–2298. https://doi.org/10.1145/2851581.2892451
Shi L, Liao Q, Lin C (2013) Survey on transformation-based large graph visualization. J Comput Aided Des Comput Graph 25(3):304–311. https://doi.org/10.3969/j.issn.1003-9775.2013.03.004
Shneiderman B, Dunne C, Sharma P et al (2012) Innovation trajectories for information visualizations: comparing treemaps, cone trees, and hyperbolic trees. Inf Vis 11(2):87–105. https://doi.org/10.1177/1473871611424815
Stasko J, Zhang E (2000) Focus + context display and navigation techniques for enhancing radial, space-filling hierarchy visualizations. In: Proceedings of IEEE symposium on information visualization. IEEE Computer Society Press, Washington, DC, pp 57–65. https://doi.org/10.1109/INFVIS.2000.885091
Sun Y, Feng X, Tang J et al (2008) Survey on the research of multidimensional and multivariate data visualization. Comput Sci 35(11):1–7. https://doi.org/10.3969/j.issn.1002-137X.2008.11.001
Sun Y, Jiang Y, Zhao X et al (2010a) Survey on the research of network visualization. Comput Sci 37(2):12–18. https://doi.org/10.3969/j.issn.1002-137X.2010.02.003
Sun Y, Zhao X, Tang J et al (2010b) Multivariate network visualization paradigm. J Softw 21(9):2250–2261. https://doi.org/10.3724/SP.J.1001.2010.03889
Van Ham F, van Wijk JJ (2003) Beamtrees: compact visualization of large hierarchies. Inf Vis 2(1):31–39. https://doi.org/10.1057/palgrave.ivs.9500036
Van Wijk JJ, Van de Wetering H (1999) Cushion treemaps: visualization of hierarchical information. In: Proceedings of IEEE symposium on information visualization. IEEE Computer Society Press, Washington, DC, pp 73–78. https://doi.org/10.1109/INFVIS.1999.801860
Vehlow C (2015) The state of the art in visualizing group structures in graphs. In: Eurographics conference on visualization. https://doi.org/10.2312/eurovisstar.20151110
Vehlow C, Beck F, Weiskopf D (2017) Visualizing group structures in graphs: a survey. Comput Graph Forum 36(6):201–225. https://doi.org/10.1111/cgf.12872
Von Landesberger T, Kuijper A, Schreck T et al (2011) Visual analysis of large graphs: state-of-the-art and future research challenges. Comput Graph Forum 30(6):1719–1749. https://doi.org/10.1111/j.1467-8659.2011.01898.x
Wang C, Tao J (2017) Graphs in scientific visualization: a survey. Comput Graph Forum 36(1):263–287. https://doi.org/10.1111/cgf.12800
Wang X, Liu S, Liu J et al (2016) TopicPanorama: a full picture of relevant topics. Visual analytics science and technology. IEEE Trans Vis Comput Graph 22(12):2508–2521. https://doi.org/10.1109/TVCG.2016.2515592
Washio T, Motoda H (2003) State of the art of graph-based data mining. ACM SIGKDD Explor Newsl 5(1):59–68. https://doi.org/10.1145/959242.959249
Wattenberg M (2002) Arc diagrams: visualizing structure in strings. In: Proceedings of IEEE symposium on information visualization. IEEE Computer Society Press, Washington, DC, pp 110–116. https://doi.org/10.1109/INFVIS.2002.1173155
Wehrend S, Lewis C (1990) A problem-oriented classification of visualization techniques. In: Proceedings of the IEEE conference on visualization, pp 139–143. http://doi.org/10.1109/VISUAL.1990.146375Wu
Wu Y, Cao N, Archambault D et al (2016) Evaluation of graph sampling: a visualization perspective. IEEE Trans Vis Comput Graph 23(1):401–410. https://doi.org/10.1109/TVCG.2016.2598867
Wu H, Jia S, Wang J et al (2018) M3: visual exploration of spatial relationships between flight trajectories. J Vis 21(3):457–470. https://doi.org/10.1007/s12650-017-0471-1
Xia J, Liu Z, Hu Y et al (2011) Hypergraph-based bone dataset visualization. J Comput Aided Des Comput Graph 23(12):2040–2045
Xiao Y, Dong H, Wu W et al (2008) Structure-based graph distance measures of high degree of precision. Pattern Recognit 41(12):3547–3561. https://doi.org/10.1016/j.patcog.2008.06.008
Xu J, Wang G, Li T et al (2017) Fat node leading tree for data stream clustering with density peaks. Knowl Based Syst 120:99–117. https://doi.org/10.1016/j.knosys.2016.12.025
Yuan X, Guo P, Xiao H et al (2009) Scattering points in parallel coordinates. IEEE Trans Vis Comput Graph 15(6):1001–1008. https://doi.org/10.1109/TVCG.2009.179
Zhang Z, McDonnell KT, Zadok E et al (2015) Visual correlation analysis of numerical and categorical data on the correlation map. IEEE Trans Vis Comput Graph 25(1):12–21. https://doi.org/10.1109/TVCG.2014.2350494
Zhao S, McGuffin MJ, Chignell MH (2005) Elastic hierarchies: combining treemaps and node-link diagrams. In: Proceedings of IEEE symposium on information visualization. IEEE Computer Society Press, Washington, DC, pp 57–64. https://doi.org/10.1109/INFVIS.2005.1532129
Zhao S, Liu X, Duan Z et al (2017) A survey on social ties mining. Chin J Comput 40(3):535–555. https://doi.org/10.11897/SP.J.1016.2017.00535
Zhao Y, Luo F, Chen M, Wang Y, Xia J et al (2018) Evaluating multi-dimensional visualizations for understanding fuzzy clusters. IEEE Trans Vis Comput Graph 21(2):289–303. https://doi.org/10.1109/TVCG.2018.2865020
Acknowledgements
This work is supported by the Basic Research Project of the Ministry of Science and Technology (Grant No. 2015FY111200), the Beijing Science and Technology Plan Project (Z161100001616004), the National Key Research and Development Program of China (Grant No. 2018YFC1603602), and the Open Funding Project of State Key Laboratory of Virtual Reality Technology and Systems, Beihang University (Grant No. BUAA-VR-17KF-07). The authors would like to thank the ChinaVis2018 conference, which provided the exchange platform for our research.
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Chen, Y., Guan, Z., Zhang, R. et al. A survey on visualization approaches for exploring association relationships in graph data. J Vis 22, 625–639 (2019). https://doi.org/10.1007/s12650-019-00551-y
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DOI: https://doi.org/10.1007/s12650-019-00551-y