1 Assorted Collaboration Network
Note: Contains IEEEVis Publication Data, DBLP collaborations, and CPAN, which contains collaboration information among developers using Perl. Origin Notes: The VIS publication data was found online in the following website: https://sites.google.com/site/vispubdata. However, we also downloaded the text file fromhttps://www.cc.gatech.edu/gvu/ii/citevis/, a tool/poster associated to the publication: http://vispubdata.org/: A Metadata Collection about IEEE Visualization (VIS) Publications. graph features handled: Categorical nodes, Directed edges, Dynamic, Hypergraphs Graph features in papers: categorical nodes,high degree,labeled nodes,generic,clusters (pre-existing),dynamic (discrete),layered graphs,n-layers,dynamic,dynamic,dynamic (continuous),clusters (generated),high degree,clusters (pre-existing),dynamic,dynamic (discrete),dynamic,dynamic (discrete),layered graphs,n-layers,clusters (generated),dynamic,dynamic (discrete),hypergraphs,layered graphs,n-layers,dynamic,dynamic (continuous),large,dynamic (discrete),dynamic,layered graphs,n-layers,weighted edges,clusters (generated),dynamic,dynamic (continuous),tripartite,clusters (pre-existing) Origin Paper: vispubdata.org: A Metadata Collection about IEEE Visualization VIS Publications (https://www.notion.so/vispubdata-org-A-Metadata-Collection-about-IEEE-Visualization-VIS-Publications-3fddec5436dc45539a82b0274ac17020?pvs=21), CPAN-explorer, an interactive exploration of the Perl Ecosystem (https://www.notion.so/CPAN-explorer-an-interactive-exploration-of-the-Perl-Ecosystem-0d1d0132440c438798a1880a6e200d8d?pvs=21), Preserving Minority Structures in Graph Sampling (https://www.notion.so/Preserving-Minority-Structures-in-Graph-Sampling-d0f9e612663d434e85599c402c9b700c?pvs=21) Originally found at: https://www.cc.gatech.edu/gvu/ii/citevis/ https://sites.google.com/site/vispubdata/home?authuser=0 https://dblp.uni-trier.de/ https://github.com/csuvis/MCGS/tree/master/dataset Size: 3503-7102 nodes, 2112-25677 edges format: Vis Pub Data text file: year range at top (1995 - 2015) then each entry is a paper
article {node ID} {doi} {some number} {paper title} {attr: val} <- then some collection of attiribute value pairs (like author, keyword…) citations: {list of line separated citations (to articles within the document) via nodeID}
Appeared in years: 2019,2006,2010,2014,2017,2007,2020,2016,2021,2022,2013,2011 Type of Collection: Aggregate collection is it stored properly?: No must be analyzed: No In repo?: Yes Related to Literature - Algorithm (1) (Dataset tag relations): Six methods for transforming layered hypergraphs to apply layered graph layout algorithms (https://www.notion.so/Six-methods-for-transforming-layered-hypergraphs-to-apply-layered-graph-layout-algorithms-0911f648bad1486fa859c9bd0de82a6b?pvs=21), Automatic Polygon Layout for Primal-Dual Visualization of Hypergraphs (https://www.notion.so/Automatic-Polygon-Layout-for-Primal-Dual-Visualization-of-Hypergraphs-09ec6b5a17ae4f5eb6aebedf0dde6467?pvs=21), Drawing Dynamic Graphs Without Timeslices (https://www.notion.so/Drawing-Dynamic-Graphs-Without-Timeslices-15310bfa5a2f4015b7a4ae0098e6c151?pvs=21), Software evolution storylines (https://www.notion.so/Software-evolution-storylines-2d8caf914a9f407482d30a7b21cf66c2?pvs=21), Content-based text mapping using multi-dimensional projections for exploration of document collections (https://www.notion.so/Content-based-text-mapping-using-multi-dimensional-projections-for-exploration-of-document-collectio-3e4cd1cd8bec41f1b819aec0330880a8?pvs=21), GraphDiaries: Animated Transitions and Temporal Navigation for Dynamic Networks (https://www.notion.so/GraphDiaries-Animated-Transitions-and-Temporal-Navigation-for-Dynamic-Networks-6c0bf6c194924eef9b824c7acb61174b?pvs=21), Storyline Visualizations with Ubiquitous Actors (https://www.notion.so/Storyline-Visualizations-with-Ubiquitous-Actors-73d912634128468b9de9775a16ed543c?pvs=21), A Maxent-Stress Model for Graph Layout (https://www.notion.so/A-Maxent-Stress-Model-for-Graph-Layout-8da4fc24a7c7438c9d10c3113841b7fd?pvs=21), TimeArcs: Visualizing Fluctuations in Dynamic Networks (https://www.notion.so/TimeArcs-Visualizing-Fluctuations-in-Dynamic-Networks-968889d3ca4a4109aca698513515e837?pvs=21), ChordLink: A New Hybrid Visualization Model (https://www.notion.so/ChordLink-A-New-Hybrid-Visualization-Model-9f4abf15618f462c962e68d2974043c9?pvs=21), Fast filtering and animation of large dynamic networks (https://www.notion.so/Fast-filtering-and-animation-of-large-dynamic-networks-a9ecbc1aa880473b834754638c54026b?pvs=21), Preserving Minority Structures in Graph Sampling (https://www.notion.so/Preserving-Minority-Structures-in-Graph-Sampling-cf062d6fa8f5484ab1190edd125f739f?pvs=21), NodeTrix: a Hybrid Visualization of Social Networks (https://www.notion.so/NodeTrix-a-Hybrid-Visualization-of-Social-Networks-e3453e98221a45d99d1aae2a9ead9d14?pvs=21) cleaned format?: Yes duplicate?: No link works?: Yes Added in paper: Yes OSF link json: https://files.osf.io/v1/resources/j7ucv/providers/osfstorage/64d90e6f4cf7480eef0556cd Origin paper plaintext: vispubdata.org: A Metadata Collection about IEEE Visualization VIS Publications, CPAN-explorer, an interactive exploration of the Perl Ecosystem, Preserving Minority Structures in Graph Sampling Page id: 6062ff126f474a50b5f3dc9b945d43da unavailable/skip: No Cleaned ALL data: No OSF link gexf: https://files.osf.io/v1/resources/j7ucv/providers/osfstorage/64d948ba94a6be101d12e880 OSF link gml: https://files.osf.io/v1/resources/j7ucv/providers/osfstorage/64d96d7d0c2b4d0f65386287 OSF link graphml: https://files.osf.io/v1/resources/j7ucv/providers/osfstorage/64d971834cf748115b055998 first look: No sparkline data: {‘min’: 3503, ‘max’: 7102, ‘step_size’: 5000, ‘num_bins’: 2, ‘bins’: [0, 5000], ‘num_nodes’: [2, 1]} Related to Literature - Algorithm (Dataset tag relations) 1: ChordLink: A New Hybrid Visualization Model0 (../Benchmark%20sets%200cc6b5e454304aec98f3b59b1a720476/Literature%20ad87f14e7097454fb2f784e2c8a2797a/Literature%20-%20Algorithm%2012e01bfc60a84007aa7d2d34293e123d/ChordLink%20A%20New%20Hybrid%20Visualization%20Model0%20dd9d1e548bfa46949279e4ecbfeb18b6.md), Content-based text mapping using multi-dimensional projections for exploration of document collections (../Benchmark%20sets%200cc6b5e454304aec98f3b59b1a720476/Literature%20ad87f14e7097454fb2f784e2c8a2797a/Literature%20-%20Algorithm%2012e01bfc60a84007aa7d2d34293e123d/Content-based%20text%20mapping%20using%20multi-dimensional%20f138d261ae1249b1bc677e59384d0b47.md), Software evolution storylines (../Benchmark%20sets%200cc6b5e454304aec98f3b59b1a720476/Literature%20ad87f14e7097454fb2f784e2c8a2797a/Literature%20-%20Algorithm%2012e01bfc60a84007aa7d2d34293e123d/Software%20evolution%20storylines%20ff8261ac4a6d498faa6fe14773b58369.md), GraphDiaries: Animated Transitions and Temporal Navigation for Dynamic Networks (../Benchmark%20sets%200cc6b5e454304aec98f3b59b1a720476/Literature%20ad87f14e7097454fb2f784e2c8a2797a/Literature%20-%20Algorithm%2012e01bfc60a84007aa7d2d34293e123d/GraphDiaries%20Animated%20Transitions%20and%20Temporal%20Nav%201db1c63ef3e3417ebc7867fcf72c3813.md), Drawing Dynamic Graphs Without Timeslices (../Benchmark%20sets%200cc6b5e454304aec98f3b59b1a720476/Literature%20ad87f14e7097454fb2f784e2c8a2797a/Literature%20-%20Algorithm%2012e01bfc60a84007aa7d2d34293e123d/Drawing%20Dynamic%20Graphs%20Without%20Timeslices%204da7155eac4b4979ae165f3b0f1d2b1b.md), NodeTrix: a Hybrid Visualization of Social Networks (../Benchmark%20sets%200cc6b5e454304aec98f3b59b1a720476/Literature%20ad87f14e7097454fb2f784e2c8a2797a/Literature%20-%20Algorithm%2012e01bfc60a84007aa7d2d34293e123d/NodeTrix%20a%20Hybrid%20Visualization%20of%20Social%20Networks%205f8162fa50f3449b89d20b25e9f2cfef.md), Storyline Visualizations with Ubiquitous Actors (../Benchmark%20sets%200cc6b5e454304aec98f3b59b1a720476/Literature%20ad87f14e7097454fb2f784e2c8a2797a/Literature%20-%20Algorithm%2012e01bfc60a84007aa7d2d34293e123d/Storyline%20Visualizations%20with%20Ubiquitous%20Actors%20fed14c376f3b4eb095022422ba483a32.md), TimeArcs: Visualizing Fluctuations in Dynamic Networks (../Benchmark%20sets%200cc6b5e454304aec98f3b59b1a720476/Literature%20ad87f14e7097454fb2f784e2c8a2797a/Literature%20-%20Algorithm%2012e01bfc60a84007aa7d2d34293e123d/TimeArcs%20Visualizing%20Fluctuations%20in%20Dynamic%20Netwo%209d27b7e02aec4b80bc15447255eb4f4c.md), Automatic Polygon Layout for Primal-Dual Visualization of Hypergraphs (../Benchmark%20sets%200cc6b5e454304aec98f3b59b1a720476/Literature%20ad87f14e7097454fb2f784e2c8a2797a/Literature%20-%20Algorithm%2012e01bfc60a84007aa7d2d34293e123d/Automatic%20Polygon%20Layout%20for%20Primal-Dual%20Visualiza%2078257e61a97c4b9ca0eb0a128ce89101.md), Six methods for transforming layered hypergraphs to apply layered graph layout algorithms (../Benchmark%20sets%200cc6b5e454304aec98f3b59b1a720476/Literature%20ad87f14e7097454fb2f784e2c8a2797a/Literature%20-%20Algorithm%2012e01bfc60a84007aa7d2d34293e123d/Six%20methods%20for%20transforming%20layered%20hypergraphs%20t%2053032e4c32f243108e20bcee60d50c11.md), A Maxent-Stress Model for Graph Layout (../Benchmark%20sets%200cc6b5e454304aec98f3b59b1a720476/Literature%20ad87f14e7097454fb2f784e2c8a2797a/Literature%20-%20Algorithm%2012e01bfc60a84007aa7d2d34293e123d/A%20Maxent-Stress%20Model%20for%20Graph%20Layout%2000c097ef77dd46d6a2df74869581a5c9.md), Preserving Minority Structures in Graph Sampling (../Benchmark%20sets%200cc6b5e454304aec98f3b59b1a720476/Literature%20ad87f14e7097454fb2f784e2c8a2797a/Literature%20-%20Algorithm%2012e01bfc60a84007aa7d2d34293e123d/Preserving%20Minority%20Structures%20in%20Graph%20Sampling%202d52160d6ac04019aaf808496f7d4240.md), Fast filtering and animation of large dynamic networks (../Benchmark%20sets%200cc6b5e454304aec98f3b59b1a720476/Literature%20ad87f14e7097454fb2f784e2c8a2797a/Literature%20-%20Algorithm%2012e01bfc60a84007aa7d2d34293e123d/Fast%20filtering%20and%20animation%20of%20large%20dynamic%20netw%2004f8b4c82871465fb46f8ad2a01d6815.md), Optimizing Stepwise Animation in Dynamic Set Diagrams (../Benchmark%20sets%200cc6b5e454304aec98f3b59b1a720476/Literature%20ad87f14e7097454fb2f784e2c8a2797a/Literature%20-%20Algorithm%2012e01bfc60a84007aa7d2d34293e123d/Optimizing%20Stepwise%20Animation%20in%20Dynamic%20Set%20Diagr%20b8b9576da7364282aeaeaf9337de8863.md), A Random Sampling O(n) Force-calculation Algorithm for Graph Layouts (../Benchmark%20sets%200cc6b5e454304aec98f3b59b1a720476/Literature%20ad87f14e7097454fb2f784e2c8a2797a/Literature%20-%20Algorithm%2012e01bfc60a84007aa7d2d34293e123d/A%20Random%20Sampling%20O(n)%20Force-calculation%20Algorithm%2086599a831f314d1cb8871a5a92420d0f.md), Event-based Dynamic Graph Drawing without the Agonizing Pain (../Benchmark%20sets%200cc6b5e454304aec98f3b59b1a720476/Literature%20ad87f14e7097454fb2f784e2c8a2797a/Literature%20-%20Algorithm%2012e01bfc60a84007aa7d2d34293e123d/Event-based%20Dynamic%20Graph%20Drawing%20without%20the%20Agon%20e67037f1481b48fab8cbd0c2802fcbe5.md), Parallel Edge Splatting for Scalable Dynamic Graph Visualization (../Benchmark%20sets%200cc6b5e454304aec98f3b59b1a720476/Literature%20ad87f14e7097454fb2f784e2c8a2797a/Literature%20-%20Algorithm%2012e01bfc60a84007aa7d2d34293e123d/Parallel%20Edge%20Splatting%20for%20Scalable%20Dynamic%20Graph%208fed21af91cf4c4aaf6a05ccb0335d43.md), ContexTour: Contextual Contour Visual Analysis on Dynamic Multi- Relational Clustering (../Benchmark%20sets%200cc6b5e454304aec98f3b59b1a720476/Literature%20ad87f14e7097454fb2f784e2c8a2797a/Literature%20-%20Algorithm%2012e01bfc60a84007aa7d2d34293e123d/ContexTour%20Contextual%20Contour%20Visual%20Analysis%20on%20D%203c45c490d66242a58cd9953fefe6c1ab.md), Comparing Node-Link and Node-Link-Group Visualizations From An Enjoyment Perspective (../Benchmark%20sets%200cc6b5e454304aec98f3b59b1a720476/Literature%20ad87f14e7097454fb2f784e2c8a2797a/Literature%20-%20Algorithm%2012e01bfc60a84007aa7d2d34293e123d/Comparing%20Node-Link%20and%20Node-Link-Group%20Visualizat%20a363d90a50ee4f04b1defe271548131c.md)
2 Body
Statistics
Description from papers
From “Drawing Dynamic Graphs Without Timeslices”
InfoVis Co-Authorship (Discrete): a co-authorship network for papers published in the InfoVis conference from 1995 to 2015 [1]. Authors collaborating on a paper are connected in a clique at the time of publication of the paper. Note this is not a cumulative network as authors can appear, disappear, and appear again. The data is of discrete nature with exactly 21 timeslices (one per year).
From “Event-based Dynamic Graph Drawing without the Agonizing Pain”
InfoVis is a co-authorship network for papers published in the InfoVis conference from 1995 to 2015 [IHK*16]. Authors on a paper are connected in a clique at the time of publication. This is not a cumulative network as authors can appear, disappear and appear again. The dataset has 21 timeslices (one per year).
2.1 Example Figures
From “Six methods for transforming layered hypergraphs to apply layered graph layout algorithms”
Fig. 7. Collaborations in papers published at VIS between a set of universities—in particular, these are the collaborators of Harvard University and the collaborators of the collaborators (up to 2 degrees of separation). This figure shows the result of the application of aggregate-collapse.
From “TimeArcs: Visualizing Fluctuations in Dynamic Networks”
Figure 5. The TimeArcs visualization applied to the IEEE VIS publication co-authorship network of the top 50 researchers from 2010 to 2014 (i.e., the same data in Fig. 4).
3 CPAN Graph Dataset
3.1 Descriptions from Literature
From Preserving Minority Structures in Graph Sampling
6.3.2 Cpan Graph Data Set
The Cpan data set is a collaboration network with 839 nodes and 2,127 edges [1]. It depicts the relationships between the developers using the same Perl modules. The original graph and samples obtained by FF, TIES, and MCGS are shown in Figure 6. This case focused on the preservation of parachute-like rims at marginal areas.
3.2 Example Figures
From Preserving Minority Structures in Graph Sampling
Fig. 6. Visual illustration of the cpan graph data set (a) and three samples generated by ff (b), ties (c), and mcgs (d) with a sampling rate of 30%.
4 DBLP
4.1 Descriptions from the Literature
From ChordLink: A New Hybrid Visualization Model
The second case study considers co-authorship networks extracted from the DBLP dataset [30], which contains publication data in computer science. Through a query consisting of keywords and Boolean operators, one can retrieve a set of publications on a desired topic. We use the results returned by DBLP to construct networks where nodes are authors and edges indicate co-authorships, weighted by the number of papers shared by their end-nodes. Nodes are labeled with authors’ names and edges with the titles of the corresponding publications. We performed the query “network AND visualization” and limited to 500 the number of search results (i.e., publications) to be returned. The resulting network consists of 1766 nodes, 3780 edges, and 382 connected components. The largest of these components contains 118 nodes and 322 edges.
4.2 Example Figures
From Parallel Edge Splatting for Scalable dynamic Graph Visualization
Fig. 8. The evolution of the word graph generated from paper titles containing the words “vis” and “web”. 21 graphs are shown for the years 1990 until 2010. A total of 264, 311 edges with weights of more than five are displayed.
From ChordLink: A New Hybrid Visualization Model
Fig. 1. A CHORDLINK visualization of a co-authorship network. The drawing has four clusters, represented as chord diagrams. In each chord diagram, circular arcs of the same color are copies of the same author. For example, in the smallest cluster, F. Montecchiani has two (green) copies, each connected to some nodes external to the cluster.
From Automatic Polygon Layout for Primal-Dual Visualization of Hypergraphs
Fig. 10. Paper and authorship data from the online database DBLP [26] for publications from 2013 to 2015 in IEEE Transactions on Pattern Analysis and Machine Intelligence. Each N-ary relationship is either a paper with N authors (left: the primal view) or an author with N papers (right: the dual view).
== STOP RENDERING ==
from Drawing Dynamic Graphs Without Timeslices
InfoVis Co-Authorship (Discrete): a co-authorship network for papers published
in the InfoVis conference from 1995 to 2015 [1]. Authors collaborating on a paper
are connected in a clique at the time of publication of the paper. Note this is
not a cumulative network as authors can appear, disappear, and appear again.
The data is of discrete nature with exactly 21 timeslices (one per year).
https://cs.swan.ac.uk/~dynnoslice/software.html
^ not really a description of the graph
from A Maxent-Stress Model for Graph Layout
ith the exception of graph gd, which is an author collaboration graph of the International Symposium on Graph Drawing between 1994 and 2007, the graphs used are from the University of Florida Sparse Matrix Collection [9]. Our selection covers a range of graph sizes, and includes mesh-like and other nonmesh graphs, and graphs from Brandes and Pich’s experimental study of distance scaling
4.3 CPAN-Explorer graphs
Link works, but subpages do not