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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 Detecting groups (communities) Sociology Biology
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Make Group Detection More Human MotaharehEslamiMehdiabadi Cutlure as Data Fall 2012
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!
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
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
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)
Data • Step 1:Interview • Name the clusters • Change the clustering as they want • Tell us their idea! • ……. • Step 2:Online Application • Join us soon…!
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 MichelineKamber. Data mining: concepts and techniques. Morgan Kaufmann, 2006. • …