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MAKE GROUP DETECTION
MORE HUMAN
Motahareh EslamiMehdiabadi
Cutlure as Data
Fall 2012
GROUPS
Communities, clusters or modules
 Community structure: many relations within a
group/ few relations between groups
 Independent Compartments
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Detecting groups (communities)
Sociology
 Biology
 Computer Science
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Hard problem
 Not yet satisfactorily solved!
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WHY DETECTING GROUPS?
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Many real networks have community structures.
Families
 Friendship circles
 Villages and Towns
 Virtual groups on internet
 …

Clustering web clients who are geographically
near to each other
 Identifying clusters of customers with similar
interests
 Ad-hoc networks
 Classification of vertices
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THE CHALLENGE
Several group detection algorithms
 No one cannot state which method (or subset of
methods) is the most reliable one in applications.
 Testing and Evaluation
 Using simple benchmark graphs

A LFR benchmark graph
Debating over complexity and time
 Limited evaluation measures
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A NEW APPROACH OF EVALUATION
Asking people to evaluate!
 Facebook Network
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Use efficient and popular algorithms
Grivan-Newman (GN)
 Markov Clustering (MCL)
 Clauset-Newman –More (CNM)
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DATA

Step 1:Interview
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Name the clusters
Change the clustering as they want
Tell us their idea!
…….
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Step 2:Online Application
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Join us soon…!
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CDA
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REFERENCES
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Fortunato, Santo. Community detection in graphs. Physics
Reports 486.3 (2010): 75-174.
Lancichinetti, Andrea, Santo Fortunato, and Filippo Radicchi.
Benchmark graphs for testing community detection algorithms.
Physical Review E 78.4 (2008): 046110.
Han, Jiawei, and Micheline Kamber. Data mining: concepts and
techniques. Morgan Kaufmann, 2006.
…
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QUESTIONS?
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