Transcript PowerPoint 演示文稿

Chapter 4
Working with NetDraw to Visualize Graphs
Hanneman, R. A., & Riddle, M. (2005). Introduction to social network methods.
(Some of the data are from the reference materials.)
Presented by Minzhe XU 1301110888
EMPHASIS
•
How to draw graphs based on social network data using NetDraw
•
How to visualize node attributes, relation properties, change position, and
highlight certain parts
•
How to draw preliminary propositions from the graph after visualization
•
What is a good drawing of a graph
CATALOG
Note that some names of the original sections have been changed.
1. Introduction
5. Location
2. Data Input
6. Highlighting Parts of the Network
3. Node Attributes
7. Output
4. Relation Properties
8. Summary
INTRODUCTION
• What is the contribution of a good drawing of a graph?
• Suggest some important features of overall network structure
• Help in understanding how a particular node is embedded
DATA INPUT
• Import data from UCINET or Pajek: “File> Open”
• Create a random network and revise: “File> Random”; “Transform>
Link Editor”; “Transform> Node Attribute Editor”
• Use an external editor to create a NetDraw Dataset
Q1: How to differentiate (in properties) the same tie representing
different relations using a text file?
NODE ATTRIBUTES
• What main node attributes can be visualized?
• Attributes based on “external” information
Whether the actor in a social network is male or female; is a 1st/ 2nd/ 3rd –
grade student; majors in arts or sciences etc.
• Attributes based on “internal” information
Whether the actor in a social network is in clique 1 or 2; has a high or
low level of power etc.
NODE ATTRIBUTES
• How to visualize attributes based on “external” information?
1. Open the data file: “Netdraw>File>Open>Uclnetdataset>Network”
2. Edit attribute data: “Transform>Node Attribute Editor>…>File>Update and Exit”
or
Create an attribute data file: “UCINET>Data>Spreadsheets>Matrix>…>Save”
and Open the attribute data file: “Netdraw>File>Open>Uclnetdataset>Attribute Data”
3. Visualizing the attributes: “Properties>Nodes>…”
NODE ATTRIBUTES
• How to visualize attributes based on “internal” information?
1. Open the data file: “Netdraw>File>Open>Uclnetdataset>Network”
2. Calculate/ use UCINET to identify internal attributes
(For k-core: “Analysis>K-core”)
3. Edit attribute data: “Transform>Node Attribute Editor>…>File>Update and Exit”
or
Create an attribute data file: “UCINET>Data>Spreadsheets>Matrix>…>Save”
and Open the attribute data file: “Netdraw>File>Open>Uclnetdataset>Attribute Data”
4. Visualizing the attributes: “Properties>Nodes>…”
NODE ATTRIBUTES
• Q2: What conjectures can you raise based on this graph?
NODE ATTRIBUTES
1. Not as institutional theory suggested, the information exchange among governmental
and non-governmental organizations seem also very common.
2. Not as ecological theory of organizations suggested, a division of “generalists” and
“specialists” seems not to affect information-sharing patterns.
3. These are simply conjectures since the number of actors is rather limited.
• Q3: How to cancel and revise previous actions of visualizing node attributes?
RELATION PROPERTIES
• What main relation properties can be visualized?
• Relation types (in a multiplex graph)
Whether the relation is between roommates, or classmates, or both, etc.
• Relation types (based on node attributes)
Whether the relation is between similar or different actors etc.
• Tie strength/ “value” of the relations
Whether the relation is very strong (5), strong (4), moderate (3), weak (2),
very weak (1), or even absent (0) etc.
RELATION PROPERTIES
• How to visualize relation types
(in a multiplex graph)?
1. Open the data files (each file
including one relation)
2. Visualizing the attributes:
“Properties>Lines>…”
RELATION PROPERTIES
• How to visualize relation types
(based on node attributes)?
1. Open data file
2. Open the attribute file
3. Visualizing the attributes:
“Properties>Lines>…”
RELATION PROPERTIES
• How to visualize reciprocal ties?
1. Open data file
2. Visualizing reciprocal ties:
“Analysis> Reciprocal Ties> …”
RELATION PROPERTIES
• How to tie strength/ “value” of
the relations?
1. Open data file (with each tie
measured with an ordinal/ interval
variable)
2. Visualizing the attributes:
“Properties>Lines>…”
LOCATION
• How can node position (in a 2 or 3 dimensional space) be
changed (with arbitrary distances between the nodes)?
• “Drag and drop” method”: Move by hand
• Random drawing: “Layout> Random”
LOCATION
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Assigning the X and Y dimensions to
attribute scores:
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“Layout> Attributes as Coordinates”
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To see how patterns of ties differ
within and between “partitions”
LOCATION
• Q4: By comparing the two graphs below, what conjectures can you raise?
LOCATION
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Circle graphs
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“Layout> Circle”
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To visualize which nodes are most
highly connected.
LOCATION
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Q5: What does the “optimization” option mean in the dialog box to draw a circle graph?
A Guess: to put the nodes which are more connected to one side (at the top left), and those which are
less connected to the other (at the bottom right).
LOCATION
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How can node position (in a 2 or 3 dimensional space) be changed (with distances
between the nodes interpreted in a meaningful way)?
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Multi-Dimensional Scaling (MDS):
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“Layout> Graph-Theoretic Layout> MDS”
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Nodes that are “more similar“ (many reasonable definitions; in this example,
referring to similar shortest paths (geodesic distances) ) are closer together.
•
Direction interpretation
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No single “correct” interpretation
LOCATION
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Q6: What conjectures can you raise based on this graph (MDS solution) ?
•
Q7: Do we only get “one” result using MDS ?
Note that node “Wro” is missed; maybe due to its long distance from other nodes.
LOCATION
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Spring-embedding:
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“Layout>Graph Theoretic Layout>Spring Embedding”
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The algorithm uses iterative fitting.
•
Nodes with smallest path lengths to one another are closest in the graph.
LOCATION
•
Q8: How is the graph below (spring-embedding solution; on the left) similar to and different from
the previous one (MDS solution; on the right) ?
Similar in shape and interpretation (distance, and direction); but different in easiness to read.
HIGHLIGHTING PARTS OF THE NETWORK
It is hard to visualize large networks in useful ways since they contain too much
information. Therefore, we need to clear away some to see important main
patterns more clearly.
•
Simplify complex diagrams
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Locate interesting sub-graphs/ “local sub-structures”
•
Ego Networks (Neighborhoods): To see how the complicated network arises
from the local connections of individual actors
HIGHLIGHTING PARTS OF THE NETWORK
How to simplify complex diagrams?
•
Combine multiple relations into an index: “Transform> Matrix Operations>
Between Datasets> Boolean Combinations” (in UNICET)
•
Q9: How to use this file
in UNICET or NetDraw
more easily?
HIGHLIGHTING PARTS OF THE NETWORK
How to simplify complex diagrams?
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Select relations you want to display
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Hide isolates and/ or pendants
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Use button-bar tools or a menu item (“Analysis> Isolates”)
HIGHLIGHTING PARTS OF THE NETWORK
How to find and visualize local sub-structures?
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Components
“Analysis> Components”
To locate the parts of graph that are
completely disconnected from one another.
HIGHLIGHTING PARTS OF THE NETWORK
How to find and visualize local sub-structures?
•
Blocks and Cutpoints:
“Analysis> Blocks & Cutpoints”
To locate parts of the graph that would become
disconnected components if either one node or
one relation were removed.
HIGHLIGHTING PARTS OF THE NETWORK
How to find and visualize local sub-structures?
•
K-cores:
“Analysis> K-Cores”
To locate parts of the graph that form sub-groups
such that each member of a sub-group is
connected to N-K of the other members.
Q10: What do you think “K” means here?
HIGHLIGHTING PARTS OF THE NETWORK
How to find and visualize local sub-structures?
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Block-based:
“Analysis> Subgroups> Block-based”
HIGHLIGHTING PARTS OF THE NETWORK
How to find and visualize local sub-structures?
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Hierarchical Clustering of Geodesic Distances:
“Analysis> Subgroups> Hiclus of Geo Distances”
To put nodes that are most similar in their profile
of distances to all other points are joined into a
cluster.
HIGHLIGHTING PARTS OF THE NETWORK
How to find and visualize local sub-structures?
•
Factions:
“Analysis> Subgroups> Factions”
To form the number of groups that you desire by
seeking to maximize connection within, and
minimize connection between the groups.
Q11: Will you always get the number of groups
you desire?
HIGHLIGHTING PARTS OF THE NETWORK
How to find and visualize local sub-structures?
•
Block Modeling:
“Analysis> Subgroups> Girvan-Newman”
With functions similar to “Fractions”
Providing measures of goodness-of-fit when
partitioning different numbers of clusters
HIGHLIGHTING PARTS OF THE NETWORK
How to find and visualize ego networks?
“Layout> Ego Networks (New)” or “Layout> Ego
Networks (Simple)”
Answering questions like “who's most connected”,
“how dense are the neighborhoods of particular
actors”, “if one node is the ego, with what
geodesic distance can the whole network be
developed” etc.
DATA OUTPUT
• Save Diagram
• Save Data
SUMMARY
• Example of drawing a graph based on data “JMS school”
• Q12: How to cluster the schools to maximize goodness-of-
fit?
• Q13: Which schools are the most connected?
• Q14: What other important analyses can you think of?
SUMMARY
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Q15: What is a good drawing of a graph/ how to properly visualize graphs?
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No single “right” way
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Easy to read (not too complicated) and draw meaningful patterns
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After a thoroughly consideration of data features, function, terminology, methods
and tools
• Two remarks:
• When facing considerable data, one can choose to combine numerical and
graphical approaches and include important nodes only.
•
When drawing patterns from the graph, one must be very cautious.
RECOMMENDED PAPER
Freeman, L. C. (2000). Visualizing social networks. Journal of social structure, 1(1), 4.
• Five fairly distinct phases in the development and use of point and line displays
in social network analysis.
• Hand drawn images
• Images grounded in computation
• Early machine generated images
• Screen oriented images
• Network images in the era of web browsers
Other Questions ?
Thank you !