Transcript Hossam Shahara, Awalin Sopan, Galileo Namata, Lise Getoor, Lisa Singh

Hossam Shahara, Awalin Sopan,
Galileo Namata, Lise Getoor, Lisa Singh
Predicting
political
preference
Labeling
paper by
topics ?
Topic 2
Topic 1
Input graph:
Partially observed
Topic 3
Output graph:
Nodes with
probability for each
possible label
?
?
Model 2
Model 1
It’s a
dog!!
It’s a
cat !!
 Why are they different ?
 Which one to rely on?
 How to improve ?
Compare uncertain graphs


Prediction from two models
Prediction with ground truth
Compare…
▪ Entire graph
▪ Individual nodes
▪ Neighborhood around the nodes
Network View
Tabular View
Matrix View
Matrix View
Columns :Model 2
Rows: Model 1
Heat map visualization
highlight cell frequencies
Tabular View
Labels
Probability
distributions
Divergence of the
distributions
Network View
Color
Best predicted label from each model
Filled area
30%
Probabilistic
Method
confidence
Confidence of prediction
Shape
Correct
Topic
90%
Theory
confidence
Divergence-> elliptical
Similarity -> circular
Border
Correct prediction
cascade of
errors
chain of
misclassification

Communication network:
 Enron

Citation:
 Citeseer

Animal Social Network:
 Dolphin network
 Understand the results of classifiers
▪ see the relational context
▪ see cascades of errors
▪ follow chains of misclassification
URL:
http://www.cs.umd.edu/projects/linqs/gpare

Catherine Plaisant and Ben Shneiderman

Janet Mann and the Shark Bay Dolphin
Research Project (SBDRP)

FODAVA grant from NSF Grant
#CCF0937094