Guillaume Erétéo, Michel Buffa, Fabien Gandon, Olivier Corby
computer-mediated networks as social networks [Wellman, 2001]
social media landscapesocial web amplifies social network effects
overwhelming flow of social data
social network analysisproposes graph algorithms to characterize the structure of a social network, strategic positions, and networking activities
social network analysisglobal metrics and structure
community detection distribution of actors and activities
density and diameter cohesion of the network
social network analysisstrategic positions and actors
degree centralitylocal attention
social network analysisstrategic positions and actors
betweenness centralityreveal broker"A place for good ideas" [Burt, 2004]
semantic social networkshttp://sioc-project.org/node/158
(guillaume)=5
Gérard
FabienMylène
MichelYvonne
father sist
er
mother
colleague
colle
ague
d
parentsibling
motherfatherbrothersister
colleague
knowsGérard
FabienMylène
MichelYvonne
father sister
mother
colleague
colle
ague
<family>d (guillaume)=3
but…SPARQL is not expressive enough to meet SNA requirements for global metric querying of social networks (density, betweenness centrality, etc.).
[San Martin & Gutierrez 2009]
classic SNA on semantic webrich graph representations reduced to simpleuntyped graphs [Paolillo & Wright, 2006]
foaf:knows
foaf:interest
semantic SNA stackexploit the semantic of social networks
SPARQL extensionsCORESE semantic search engine implementing semantic web languagesusing graph-based representations
grouping resultsnumber of followers of a twitter user
select ?y count(?x) as ?indegree where{
?x twitter:follow ?y
} group by ?y
path extractionpeople knowing, knowing, (...) colleagues of someone
?x sa (foaf:knows*/rel:worksWith)::$path ?yfilter(pathLength($path) <= 4)
Regular expression operators are: / (sequence) ; | (or) ; * (0 or more) ; ? (optional) ; ! (not)
Path characteristics: i to allow inverse properties, s to retrieve only one shortest path, sa to retrieve all shortest paths.
full examplecloseness centrality through knows and worksWith
select distinct ?y ?to pathLength($path) as ?length (1/sum(?length)) as ?centrality
where{?y s (foaf:knows*/rel:worksWith)::$path ?to
}group by ?y
1
GExworksWithknows
cworksWithknows xkglengthkC ,/*/*
Qualified component
Qualified in-degree
Qualified diameter
Closenness Centrality
Betweenness Centrality
Number of geodesics between from and to
Qualified degree
Number of geodesics between from and togoing through b
SemSNA an ontology of SNAhttp://ns.inria.fr/semsna/2009/06/21/voc
add to the RDF graphsaving the computed degrees for incremental calculations
CONSTRUCT{ ?y semsna:hasSNAConcept _:b0 _:b0 rdf:type semsna:Degree _:b0 semsna:hasValue ?degree _:b0 semsna:isDefinedForProperty rel:family}SELECT ?y count(?x) as ?degree where{ { ?x rel:family ?y } UNION { ?y rel:family ?x }}group by ?y
sister
mother
supervisor
hasSNAConcept
isDefinedForProperty
hasValue
4
colleaguecolleague
father
Philippe
hasCentralityDistance
colleague
2
colleague
supervisorcolleague
supervisor
Degree
Guillaume
Gérard
Fabien
Mylène
Michel
Yvonne
IvanPeter
Ipernity
using real dataextracting a real dataset from a relational database
construct { ?person1 rel:friendOf ?person2 }
select sql(<server>, <driver>, <user>, <pwd>, select user1_id, user2_id
from relations where rel = 1 ') as (?person1 , ?person2 ) where {}
importing data with SemSNIhttp://ns.inria.fr/semsni/
using real dataipernity.com dataset extracted in RDF61 937 actors & 494 510 relationships–18 771 family links between 8 047 actors–136 311 friend links implicating 17 441 actors –339 428 favorite links for 61 425 actors–2 874 170 comments from 7 627 actors–795 949 messages exchanged by 22 500 actors
performances & limits Knows 0.71 s 494 510Favorite 0.64 s 339 428Friend 0.31 s 136 311Family 0.03 s 18 771Message 1.98 s 795 949Comment 9.67 s 2 874 170Knows 20.59 s 989 020Favorite 18.73 s 678 856Friend 1.31 s 272 622Family 0.42 s 37 542Message 16.03 s 1 591 898Comment 28.98 s 5 748 340
Shortest paths used to calculate
Knows Path length <= 2: 14m 50.69s Path length <= 2: 2h 56m 34.13sPath length <= 2: 7h 19m 15.18s
100 0001 000 0002 000 000
Favorite Path length <= 2: 5h 33m 18.43s 2 000 000Friend Path length <= 2: 1m 12.18 s
Path length <= 2: 2m 7.98 s1 000 0002 000 000
Family Path length <= 2 : 27.23 sPath length <= 2 : 2m 9.73 sPath length <= 3 : 1m 10.71 sPath length <= 4 : 1m 9.06 s
1 000 0003 681 6261 000 0001 000 000
)(GComp rel
)(, yD rel 1
)(bC relb
time projections
some interpretationsvalidated with managers of ipernity.comfriendOf, favorite, message, comment
small diameter, high densityfamily as expected: large diameter, low densityfavorite: highly centralized around Ipernity animator. friendOf, family, message, comment: power law of degrees
and betweenness centralities, different strategic actorsknows: analyze all relations using subsumption
some interpretationsexistence of a largest component in all sub networks"the effectiveness of the social network at doing its job" [Newman 2003]
0100002000030000
40000500006000070000
number actors size largest component
knows
favorite
friend
family
message
comment
conclusion
directed typed graph structure of RDF/S well suited to represent social knowledge & socially produced metadata spanning both internet and intranet networks.
definition of SNA operators in SPARQL (using extensions and OWL Lite entailment) enable to exploit the semantic structure of social data.
SemSNAorganize and structure social data.
perspectives semantic based community detection algorithm
SemSNA Ontologyextract complex SNA features reusing past results
support iterative or parallel approaches in the computations
a semantic SNA to foster a semantic intranet of people structure overwhelming flows of corporate social data
foster and strengthen social interactions
efficient access to the social capital [Krebs, 2008]
built through online collaboration
http://twitter.com/isicil
nameGuillaume Erétéo
holdsAccount
organization
mentorOf
mentorOf
holdsAccount
manage
contribute
contribute
answers
twitter.com/ereteog slideshare.net/ereteog