Make Group Detection More Human
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Transcript of Make Group Detection More Human
MAKE GROUP DETECTION MORE HUMAN
Motahareh EslamiMehdiabadi
Cutlure as Data
Fall 2012
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GROUPS
Communities, clusters or modules Community structure: many relations within
a group/ few relations between groups Independent Compartments
Detecting groups (communities) Sociology Biology Computer Science
Hard problem Not yet satisfactorily solved!
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WHY DETECTING GROUPS?
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
Debating over complexity and time Limited evaluation measures
A LFR benchmark graph
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A NEW APPROACH OF EVALUATION
Asking people to evaluate! Facebook Network
Use efficient and popular algorithms Grivan-Newman (GN) Markov Clustering (MCL) Clauset-Newman –More (CNM)
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DATA
Step 1:Interview Name the clusters Change the clustering as they want Tell us their idea! …….
Step 2:Online Application
Join us soon…!
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CDA
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REFERENCES 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?