Visual Analysis of Set Relations in a Graph
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1 Visual Analysis of Set Relations in a Graph Panpan Xu 1, Fan Du 2, Nan Cao 3, Conglei Shi 1, Hong Zhou 4, Huamin Qu 1 1 Hong Kong University of Science and Technology, 2 Zhejiang University, 3 IBM T. J. Watson Research Center, 4 Shenzhen University
2 Motivation: data model and research questions Approaches Previous works Technical details Case studies Limitation and future works Outline
3 Motivation: data model and research questions Approaches Previous works Technical details Case studies Limitation and future works Outline
4 Collaboration network Research topics tree graph hierarchical data pipeline architecture Data
5 Collaboration network Research topics tree graph hierarchical data pipeline architecture Data
6 Homophily effect Research questions
7 Homophily effect Do birds of a feather flock together? Research questions
8 Homophily effect Do birds of a feather flock together? How proximity of nodes correlates to set relation? Research questions
9 Homophily effect Do birds of a feather flock together? How proximity of nodes correlates to set relation? Set relation over item clusters Distribution and implicit overlap of the sets Research questions
10 Homophily effect Do birds of a feather flock together? How proximity of nodes correlates to set relation? Set relation over item clusters Distribution and implicit overlap of the sets complementary perspectives Research questions
11 Motivation: data model and research questions Approaches Previous works Technical details Case studies Limitation and future works Outline
12 Homophily effect Set relation over item clusters Approaches
13 Homophily effect Glyph design at graph nodes correlates set relation and node distance Set relation over item clusters Approaches
14 Homophily effect Glyph design at graph nodes correlates set relation and node distance Set relation over item clusters Approaches
15 Homophily effect Glyph design at graph nodes correlates set relation and node distance Set relation over item clusters Contour map + visual link design Layout algorithm trades precise location of the items for visual simplicity (inspired by metro map drawing, storyline visualization) Approaches
16 Homophily effect Glyph design at graph nodes correlates set relation and node distance Set relation over item clusters Contour map + visual link design Layout algorithm trades precise location of the items for visual simplicity (inspired by metro map drawing, storyline visualization) Approaches
17 Motivation: data model and research questions Approaches Previous works Technical details Case studies Limitation and future works Outline
18 PivotPath [Dörk et al. 12] Facetatlas [Cao et al. 10] Previous works graph visualization
19 PivotPath [Dörk et al. 12] GraphDice [Bezerianos et al. 10] Facetatlas [Cao et al. 10] Previous works graph visualization
20 Untangling Euler diagrams [Riche and Dwyer, 10] Previous works set visualization
21 Untangling Euler diagrams [Riche and Dwyer, 10] Line Set [Alper et al., 11] Bubble Set [Collins et al., 09] Kelp Diagram [Dinkla et al., 12] Previous works set visualization
22 Motivation: data model and research questions Approaches Previous works Technical details Case studies Limitation and future works Outline
23 Correlate set overlap and node distance Scatterplot Glyph design
24 Correlate set overlap and node distance more compact Shade amount of set overlap Height the number of nodes at same distances and with similar amount of overlap Scatterplot Stacked Barchart Glyph design
25 Correlate set overlap and node distance more compact Scatterplot Stacked Barchart Stacked Graph Glyph design
26 Draw glyphs for each node on a graph Hue: size of the set compared to its neighbors Glyph design
27 lots of overlap with distant nodes community with locally distributed interests Draw glyphs for each node on a graph Hue: size of the set compared to its neighbors Glyph design
28 Layout Items Generate contour map Form backbone spanning tree Route visual links Visually summarize item clusters Layout visual links for sets Set visualization over item clusters Result
29 tree graph hierarchical data pipeline architecture MDS: similar items form visual clusters Layout Items Generate contour map Form backbone spanning tree Route visual links
30 tree graph hierarchical data pipeline architecture MDS: similar items form visual clusters Layout Items Generate contour map Form backbone spanning tree Route visual links
31 tree graph hierarchical data pipeline Contour map with KDE: abstracted display of item clusters architecture MDS: similar items form visual clusters Layout Items Generate contour map Form backbone spanning tree Route visual links
32 Contour map with KDE: abstracted display of item clusters tree graph hierarchical data form context for drawing the sets pipeline architecture MDS: similar items form visual clusters Layout Items Generate contour map Form backbone spanning tree Route visual links
33 tree graph hierarchical data pipeline architecture Layout Items Generate contour map Form backbone spanning tree Route visual links
34 tree graph hierarchical data pipeline architecture Form MST for items in selected sets Layout Items Generate contour map Form backbone spanning tree Route visual links
35 tree graph hierarchical data Fold small branches on MST pipeline architecture Form MST for items in selected sets Layout Items Generate contour map Form backbone spanning tree Route visual links
36 tree graph hierarchical data Fold small branches on MST pipeline architecture Form MST for items in selected sets Straighten branches Layout Items Generate contour map Form backbone spanning tree Route visual links
37 tree graph hierarchical data Fold small branches on MST pipeline architecture Form MST for items in selected sets segment Straighten branches Layout Items Generate contour map Form backbone spanning tree Route visual links
38 Draw visual link for individual sets Layout Items Generate contour map Form backbone spanning tree Route visual links
39 Layout Items Generate contour map Form backbone spanning tree Route visual links
40 the original MST and the simplified backbone the visual links for three sets Layout Items Generate contour map Form backbone spanning tree Route visual links
41 Motivation: data model and research questions Approaches Previous works Technical details Case studies Limitation and future works Outline
42 Bibliographic data Infovis proceedings (95-02) Titles, Authors Abstracts, References
43 unique interests overlap of distant nodes Infovis proceedings (95-02) Titles, Authors Abstracts, References Bibliographic data
44 Social site data Last.fm Artist similarity User friendship Listening history
45 Social site data Last.fm Artist similarity User friendship Listening history
46 Glyph design for homophily analysis Set visualization over item clusters and layout algorithm Case studies Summary
47 Motivation: data model and research questions Approaches Previous works Technical details Case studies Limitation and future works Outline
48 Scalability of glyph design Use different graph layout, aggregate the nodes Limitation & future works
49 Scalability of glyph design Use different graph layout, aggregate the nodes Scalability of set visualization Improve layout algorithm Limitation & future works
50 Scalability of glyph design Use different graph layout, aggregate the nodes Scalability of set visualization Improve layout algorithm Evaluation Compare with existing techniques (Line set, Kelp diagram) Limitation & future works
51 Scalability of glyph design Use different graph layout, aggregate the nodes Scalability of set visualization Improve layout algorithm Evaluation Compare with existing techniques (Line set, Kelp diagram) Application of set visualization technique Draw sets on word cloud, tree map, etc. Limitation & future works
52 Thanks! panpan
53 Last.fm Data Artist similarity collected through Last.fm web API User information could also be accessed Infovis 04 publication data Keyword similarity: through topic modeling (LDA) and co-citation Dataset collection & processing
54 Metro map drawing
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