Community Mining in Virtual Social
Networks: Trends and Challenges
Asmae El Kassiri and Fatima-Zahra Belouadha
Siweb research team, Computer Science Department
Ecole Mohammadia d’Ingénieurs, Mohammed V- Agdal University
Rabat, Morocco
asmaekassiri@gmail.com, belouadha@emi.ac.ma
A. El Kassiri, PhD student, Research Interest: Semantic community mining
F. Belouadha, Habilitated Professor, Research Interests: Semantic Web service
composition, BPM, Cloud computing and semantic data mining
Data mining in VSN: interest and
purposes
 VSN refers to networks of people interacting online using
e.g. social media.
 Interest:
 VSN contain a large volume of important information which
can be exploited in various areas e.g. marketing and politics.
 Purposes:




Detection of important nodes of the network
Opinion mining
Community mining based on clustering techniques.
etc.
Asmae EL KASSIRI and Fatima-Zahra BELOUADHA. Oracle Presentation. March 2014.
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Clustering: definition and types
 Clustering is a division of data into groups (clusters) of similar
objects.
 It consists in three iterative stages and leads to hard or fuzzy
clusters.
Asmae EL KASSIRI and Fatima-Zahra BELOUADHA. Oracle Presentation. March 2014.
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Structural analysis
 It is based on the graph theory and calculates similarity
according to the VSN topology.
 It does not consider the semantics of the information included in
VSN.
Asmae EL KASSIRI and Fatima-Zahra BELOUADHA. Oracle Presentation. March 2014.
3
Semantic analysis
 It is based on the semantic Web.
 It uses ontologies to represent the network.
 It adopts a semantic clustering.
Asmae EL KASSIRI and Fatima-Zahra BELOUADHA. Oracle Presentation. March 2014.
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Related work
 Specific or generic ontologies are
used to represent the VSN.
 music ontology,event ontology,
SCOT, MOAT…
 Profiles, activities and tags (FOAF,
SIOC, semSNI, SKOS)
 Semantic clustering based on:
 a two phase-algorithm using both
semantic and structural measures
 semantic structural measures
(semantic modularity and centrality).
Asmae EL KASSIRI and Fatima-Zahra BELOUADHA. Oracle Presentation. March 2014.
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VSN Semantic Analysis Issues
 What semantic model is it appropriate to represent the VSN
data?
 What similarity measure is it adapted to analyse the VSN
data?
 At what level of the clustering algorithm, is it appropriate to
integrate the semantic dimension?
Asmae EL KASSIRI and Fatima-Zahra BELOUADHA. Oracle Presentation. March 2014.
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Conclusions
 Standard-based extended generic ontology
 Able to describe the contents of different social media
 Makes easy the aggregation of the contents describing the behavior of a
person, from a set of social media of which he is a member.
 Issues:
 What semantic model is it appropriate to represent the VSN data?
 What similarity measure is it adapted to analyse the VSN data?
 At what level of the clustering algorithm, is it appropriate to integrate
the semantic dimension?
Asmae EL KASSIRI and Fatima-Zahra BELOUADHA. Oracle Presentation. March 2014.
7
Semantic analysis
 It is based on the semantic Web.
 It uses ontologies to represent the
network.
 It adopts a semantic clustering
based on:
 only semantic distance
 a two phase-algorithm using both
semantic and structural measures
 semantic structural measures.
Asmae EL KASSIRI and Fatima-Zahra BELOUADHA. Oracle Presentation. March 2014.
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