WORDBRIDGE     USING COMPOSITE TAG CLOUDS IN NODE-LINK            DIAGRAMS FOR VISUALIZING     CONTENT AND RELATIONS IN TE...
Me          Niklas     Pete
best friend, car, gamesMe                                               Niklas     work                             neighb...
Me                    best friend car games best friend                    Niklas                                         ...
WORDBRIDGE
WORDBRIDGE
WORDBRIDGE
APPROACHESText mining  Term Frequency-Inverse Document Frequency (e.g. Salton et al. 1988)Text visualization  Wordle (Vieg...
DATA STRUCTUREStandard graph structure         G = (V, E)Each of vertex and edge contains a tuple (word, rank)            ...
DATASET EXAMPLEDatasets          Node Cloud                Link Cloud  Social        Keywords from status     Keywords fro...
WORDBRIDGE OVERVIEW
WORDBRIDGE LAYOUTR1. Constrained LayoutR2. Space-efficientR3. Computationally EfficientR4. Variable shape and sizeR5. Determ...
WORDLE LAYOUT
WORDLE LAYOUT
WORDLE LAYOUT
WORDLE LAYOUT
William Shakespeare’s “A Midsummer Night’s Dream”             Point layout with random grayscale color
William Shakespeare’s “A Midsummer Night’s Dream”              Line layout with random grayscale color
EXAMPLES OF WORDBRIDGEHomer’s “Odyssey”“The Adventure of Huckleberry Finn” by Mark Twain“The Adventure of Tom Sawyer” by M...
APROPOS
APROPOS
APROPOS
APROPOS
APROPOS
Homer’s Odyssey
The Adventure of Tom Sawyer by Mark Twain
King James Bible
Blue Iguanodon from VAST 2007 challenge
CONCLUSIONWordBridge = Structure + Keywords
Contact Information email : kimk@purdue.edu    elm@purdue.eduLike PivotLab from Facebook
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  • \n
  • We all know node-link diagram is very effective to show relations between entities and give overview of it. Node represents entities, and link represents relation between them. It is one of the most common visual represenation that is used in our daily life. For example, this graph shows overview of social network from Facebook. It connects friends with edges to show how they are related. There is a wide variety of usage of node-link diagrams, such as GPS road network, computer network and airline connection. \n
  • (Do I need this slide) Figure reference\nVery basic components of a node-link diagram. The diagram shows how entities are connected to each other, the table on the right shows weights of the edges. In order to find out information of the edges is to look up the table to find out the weight.\n
  • Let say this is portion of a social network\n\nFor example, this is an example graph of a social network. Here is showing relation among three friends. But with the traditional node-link diagram does not fully expose information of the network, it is only saying the they are somehow connected. But if we have data to characterize the edges such as, posts on the wall, we can extract keywords from the posts and put label on the edges.\n\nOur motivation started from here to find a better way to give an overview of the network which has much more data to show. So making bridges with words, we named it ‘WordBridge’.\n
  • Let say this is portion of a social network\n\nFor example, this is an example graph of a social network. Here is showing relation among three friends. But with the traditional node-link diagram does not fully expose information of the network, it is only saying the they are somehow connected. But if we have data to characterize the edges such as, posts on the wall, we can extract keywords from the posts and put label on the edges.\n\nOur motivation started from here to find a better way to give an overview of the network which has much more data to show. So making bridges with words, we named it ‘WordBridge’.\n
  • Let say this is portion of a social network\n\nFor example, this is an example graph of a social network. Here is showing relation among three friends. But with the traditional node-link diagram does not fully expose information of the network, it is only saying the they are somehow connected. But if we have data to characterize the edges such as, posts on the wall, we can extract keywords from the posts and put label on the edges.\n\nOur motivation started from here to find a better way to give an overview of the network which has much more data to show. So making bridges with words, we named it ‘WordBridge’.\n
  • Let say this is portion of a social network\n\nFor example, this is an example graph of a social network. Here is showing relation among three friends. But with the traditional node-link diagram does not fully expose information of the network, it is only saying the they are somehow connected. But if we have data to characterize the edges such as, posts on the wall, we can extract keywords from the posts and put label on the edges.\n\nOur motivation started from here to find a better way to give an overview of the network which has much more data to show. So making bridges with words, we named it ‘WordBridge’.\n
  • Let say this is portion of a social network\n\nFor example, this is an example graph of a social network. Here is showing relation among three friends. But with the traditional node-link diagram does not fully expose information of the network, it is only saying the they are somehow connected. But if we have data to characterize the edges such as, posts on the wall, we can extract keywords from the posts and put label on the edges.\n\nOur motivation started from here to find a better way to give an overview of the network which has much more data to show. So making bridges with words, we named it ‘WordBridge’.\n
  • Let say this is portion of a social network\n\nFor example, this is an example graph of a social network. Here is showing relation among three friends. But with the traditional node-link diagram does not fully expose information of the network, it is only saying the they are somehow connected. But if we have data to characterize the edges such as, posts on the wall, we can extract keywords from the posts and put label on the edges.\n\nOur motivation started from here to find a better way to give an overview of the network which has much more data to show. So making bridges with words, we named it ‘WordBridge’.\n
  • Let say this is portion of a social network\n\nFor example, this is an example graph of a social network. Here is showing relation among three friends. But with the traditional node-link diagram does not fully expose information of the network, it is only saying the they are somehow connected. But if we have data to characterize the edges such as, posts on the wall, we can extract keywords from the posts and put label on the edges.\n\nOur motivation started from here to find a better way to give an overview of the network which has much more data to show. So making bridges with words, we named it ‘WordBridge’.\n
  • Let say this is portion of a social network\n\nFor example, this is an example graph of a social network. Here is showing relation among three friends. But with the traditional node-link diagram does not fully expose information of the network, it is only saying the they are somehow connected. But if we have data to characterize the edges such as, posts on the wall, we can extract keywords from the posts and put label on the edges.\n\nOur motivation started from here to find a better way to give an overview of the network which has much more data to show. So making bridges with words, we named it ‘WordBridge’.\n
  • Let say this is portion of a social network\n\nFor example, this is an example graph of a social network. Here is showing relation among three friends. But with the traditional node-link diagram does not fully expose information of the network, it is only saying the they are somehow connected. But if we have data to characterize the edges such as, posts on the wall, we can extract keywords from the posts and put label on the edges.\n\nOur motivation started from here to find a better way to give an overview of the network which has much more data to show. So making bridges with words, we named it ‘WordBridge’.\n
  • Let say this is portion of a social network\n\nFor example, this is an example graph of a social network. Here is showing relation among three friends. But with the traditional node-link diagram does not fully expose information of the network, it is only saying the they are somehow connected. But if we have data to characterize the edges such as, posts on the wall, we can extract keywords from the posts and put label on the edges.\n\nOur motivation started from here to find a better way to give an overview of the network which has much more data to show. So making bridges with words, we named it ‘WordBridge’.\n
  • Let say this is portion of a social network\n\nFor example, this is an example graph of a social network. Here is showing relation among three friends. But with the traditional node-link diagram does not fully expose information of the network, it is only saying the they are somehow connected. But if we have data to characterize the edges such as, posts on the wall, we can extract keywords from the posts and put label on the edges.\n\nOur motivation started from here to find a better way to give an overview of the network which has much more data to show. So making bridges with words, we named it ‘WordBridge’.\n
  • Let say this is portion of a social network\n\nFor example, this is an example graph of a social network. Here is showing relation among three friends. But with the traditional node-link diagram does not fully expose information of the network, it is only saying the they are somehow connected. But if we have data to characterize the edges such as, posts on the wall, we can extract keywords from the posts and put label on the edges.\n\nOur motivation started from here to find a better way to give an overview of the network which has much more data to show. So making bridges with words, we named it ‘WordBridge’.\n
  • That was the basic approach of the WordBridge. This is one of example of the WordBridge showing co-authorship network. Node cloud which is in orange convex is showing author name in the middle and shows individual work around of the name. While ‘Link Cloud’ which is in blue convex reveals joint project. By using the WordBridge, user will be able to find out not only those connected to each other but also why and how they are connected. In this example, we have entered the dataset manually based on there homepage. In order to create tag clouds for nodes and links, we came up with simple data-structure which can be used in our implementation.\n
  • That was the basic approach of the WordBridge. This is one of example of the WordBridge showing co-authorship network. Node cloud which is in orange convex is showing author name in the middle and shows individual work around of the name. While ‘Link Cloud’ which is in blue convex reveals joint project. By using the WordBridge, user will be able to find out not only those connected to each other but also why and how they are connected. In this example, we have entered the dataset manually based on there homepage. In order to create tag clouds for nodes and links, we came up with simple data-structure which can be used in our implementation.\n
  • wordle beautified tag cloud\nphrasenet relationship between words\nwordtree shows unstructured text in structured format using hierarchical tree\n\ngreenarrow replaces edges with labels\nptc shows relationship between multi dimension\n\nOur contribution is to present a novel graph-based visualization technique for shoing relationships between entities in text corpra.\n
  • We maintained the simple graph structure which has information of vertices and edges. In addition to that, WordBridge vertices and edges also have tuples of keywords and its rank which is used to create tag clouds. For instance Tv node cloud will characterize node itself, and Te(V1, V2) will characterize relationship between v1 and v2.\n
  • Any of dataset can be applied to the WordBridge. For example as we showed you before, \n
  • This is a quick overview of wordbridge in action. As you can see adapted force direct layout to distribute the nodes effectively. Edges of Force Directed Layout work as springs which repels each other nodes to distribute themselves.\n
  • WordBridge can use any of graph layout algorithm,but in order to fulfill our need, we came up with five design requirement for a layout algorithm. R1 Constrained Layout is because we are creating lines or clouds with words, so we want to be able to control \n\nTrick here is to show \n
  • Keep a list of available areas\n
  • Keep a list of available areas\n
  • Keep a list of available areas\n
  • \n
  • \n
  • \n
  • Apropos is an implementation of the WordBridge. flash web-browser\n
  • Apropos is an implementation of the WordBridge. flash web-browser\n
  • Apropos is an implementation of the WordBridge. flash web-browser\n
  • Apropos is an implementation of the WordBridge. flash web-browser\n
  • Apropos is an implementation of the WordBridge. flash web-browser\n
  • Apropos is an implementation of the WordBridge. flash web-browser\n
  • Apropos is an implementation of the WordBridge. flash web-browser\n
  • \n
  • Thickness of edge shows how strongly two componets are connected\n\n “Huck and Tom observe murder of Joe”\n
  • \n
  • Fictional terrorist organization trying to poison us food.\n
  • State problem \nreading all documents is impossible\nJust exacting keyword or structure is not enough. WordBridge combines keywords and structure between entities in a visualization.\n\nApproach is basically combining node-link diagram \n
  • \n
  • Wordbridge hicss2010

    1. 1. WORDBRIDGE USING COMPOSITE TAG CLOUDS IN NODE-LINK DIAGRAMS FOR VISUALIZING CONTENT AND RELATIONS IN TEXT CORPORAKyungTae Kim, SungAhn Ko, Niklas Elmqvist and David S. Ebert Purdue University
    2. 2. Me Niklas Pete
    3. 3. best friend, car, gamesMe Niklas work neighbor Pete
    4. 4. Me best friend car games best friend Niklas r bo wo igh rkw ne or or kw hb eig or kw rn bo or kw h eig or rn kw bo or igh k ne Pete
    5. 5. WORDBRIDGE
    6. 6. WORDBRIDGE
    7. 7. WORDBRIDGE
    8. 8. APPROACHESText mining Term Frequency-Inverse Document Frequency (e.g. Salton et al. 1988)Text visualization Wordle (Viegas et al. 2008) PhraseNet (Ham et al. 2009) Word Tree (Wattenberg et al. 2008)Text relations GreenArrow (Wong et al. 2005) Parallel Tag Cloud (Collins et al. 2009)
    9. 9. DATA STRUCTUREStandard graph structure G = (V, E)Each of vertex and edge contains a tuple (word, rank) T ⇢W⇥R Tv (v) characterize itselfTe (v1 , v2 ) characterizes relationship
    10. 10. DATASET EXAMPLEDatasets Node Cloud Link Cloud Social Keywords from status Keywords from posts Network messages between friends Keywords from Keywords fromInvestigative documents which documents which Analysis contains name of an contains both on name of entity nodesCo-author Individual Work Joint Project
    11. 11. WORDBRIDGE OVERVIEW
    12. 12. WORDBRIDGE LAYOUTR1. Constrained LayoutR2. Space-efficientR3. Computationally EfficientR4. Variable shape and sizeR5. Deterministic
    13. 13. WORDLE LAYOUT
    14. 14. WORDLE LAYOUT
    15. 15. WORDLE LAYOUT
    16. 16. WORDLE LAYOUT
    17. 17. William Shakespeare’s “A Midsummer Night’s Dream” Point layout with random grayscale color
    18. 18. William Shakespeare’s “A Midsummer Night’s Dream” Line layout with random grayscale color
    19. 19. EXAMPLES OF WORDBRIDGEHomer’s “Odyssey”“The Adventure of Huckleberry Finn” by Mark Twain“The Adventure of Tom Sawyer” by Mark TwainKing James BibleBlue Iguanodon
    20. 20. APROPOS
    21. 21. APROPOS
    22. 22. APROPOS
    23. 23. APROPOS
    24. 24. APROPOS
    25. 25. Homer’s Odyssey
    26. 26. The Adventure of Tom Sawyer by Mark Twain
    27. 27. King James Bible
    28. 28. Blue Iguanodon from VAST 2007 challenge
    29. 29. CONCLUSIONWordBridge = Structure + Keywords
    30. 30. Contact Information email : kimk@purdue.edu elm@purdue.eduLike PivotLab from Facebook
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