Full text in OPEN ACCESS mode http://www.springerlink.com/content/d6771886878t8p10/fulltext.pdf The continuous interest in the social network area contributes to the fast development of this field. The new possibilities of obtaining and storing data facilitate deeper analysis of the entire network, extracted social groups and single individuals as well. One of the most interesting research topic is the dynamics of social groups, it means analysis of group evolution over time. Having appropriate knowledge and methods for dynamic analysis, one may attempt to predict the future of the group, and then manage it properly in order to achieve or change this predicted future according to specific needs. Such ability would be a powerful tool in the hands of human resource managers, personnel recruitment, marketing, etc. The social group evolution consists of individual events and seven types of such changes have been identified in the paper: continuing, shrinking, growing, splitting, merging, dissolving and forming. To enable the analysis of group evolution a change indicator – inclusion measure was proposed. It has been used in a new method for exploring the evolution of social groups, called Group Evolution Discovery (GED). http://www.ii.pwr.wroc.pl/~brodka/ged.php

1. GED: The Method for
Group Evolution Discovery
in Social Networks
Piotr Bródka, Stanisław Saganowski, Przemysław Kazienko
Social Network Analysis and Mining,
DOI:10.1007/s13278-012-0058-8
piotr.brodka@pwr.wroc.pl,
stanislaw.saganowski@pwr.wroc.pl, kazienko@pwr.wroc.pl

2. Agenda
Problem description and motivation
Basic concepts
Group evolution
Inclusion measure
GED
Experiments
Final remarks

3. Tracking Group Evolution in
Social Networks
• Groups extraction is nice
• … but group evolution
prediction is nicer …
group quantity
 
 
• … so we need to identify | G1 G2 | SPG1 ( x)
x (G1 G 2 )
I (G1 , G2 )
| G1 | SPG1 ( x)
changes in group evolution.
1 ) 
x (G
 
group quality

4. Basic Concepts: Social Network
Social network: SN(V,E)
V – a set of vertices
E – a set of directed
edges <x,y>:x,y V

5. Basic Concepts:
Temporal Social Network
TSN - a list of following timeframes
(time windows) T, each is a social
network SN(V,E)

6. Basic Concepts:
Group (Community)
• No commonly accepted group
definition
• A group is a set of people, who
have strong mutual (internal)
relationships and weak with people
outside the group (external)
• Group G in the social network SN(V,E) is a
subset of vertices (G V), extracted using
any method (clustering algorithm)

7. Group Evolution
• Group evolution is a sequence of events
succeeding each other in the successive
time windows within TSN
• Continuing
• Shrinking
• Growing
• Splitting
• Merging
• Dissolving
• Forming

8. Group Evolution
• Group evolution is a sequence of events
succeeding each other in the successive
time windows within TSN
• Continuing
• Shrinking
• Growing
• Splitting
• Merging
• Dissolving
• Forming

9. Group Evolution
• Group evolution is a sequence of events
succeeding each other in the successive
time windows within TSN
• Continuing
• Shrinking
• Growing
• Splitting
• Merging
• Dissolving
• Forming

10. Group Evolution
• Group evolution is a sequence of events
succeeding each other in the successive
time windows within TSN
• Continuing
• Shrinking
• Growing
• Splitting
• Merging
• Dissolving
• Forming

11. Group Evolution
• Group evolution is a sequence of events
succeeding each other in the successive
time windows within TSN
• Continuing
• Shrinking
• Growing
• Splitting
• Merging
• Dissolving
• Forming

12. Group Evolution
• Group evolution is a sequence of events
succeeding each other in the successive
time windows within TSN
• Continuing
• Shrinking
• Growing
• Splitting
• Merging
• Dissolving
• Forming

13. Group Evolution
• Group evolution is a sequence of events
succeeding each other in the successive
time windows within TSN
• Continuing
• Shrinking
• Growing
• Splitting
• Merging
• Dissolving
• Forming

14. GED Method:
Introduction
• GED (Group Evolution Discovery) method
takes into account
– quantity of the group members
– quality of the group members
• Members quality: any centrality measure
– social position and degree centrality measures
was utilized in the experiments

15. GED method:
Inclusion measure
group quantity
 
 
| G1 G2 | SPG1 ( x)
x (G1 G 2 )
I (G1 , G2 )
| G1 | SPG1 ( x)
x (G1 )
 
 
group quality
Group quantity
Group quality

16. GED - Group Evolution
Discovery Method
Input: TSN in which at each timeframe Ti groups are extracted by any community detection
algorithm. Calculated any user importance measure.
For each pair of groups <G1, G2> in consecutive timeframes Ti and Ti+1 inclusion of G1 in G2
and G2 in G1 is counted according to equations (3).
Based on inclusion and size of two groups one type of event may be assigned:
Continuing: I(G1,G2) α and I(G2,G1) β and |G1| = |G2|
Shrinking: I(G1,G2) α and I(G2,G1) β and |G1| > |G2| OR I(G1,G2) < α and I(G2,G1)
β and |G1| |G2| and there is only one match (matching event) between G2 and all
groups in the previous time window Ti
Growing: I(G1,G2) α and I(G2,G1) β and |G1|<|G2| OR I(G1,G2) α and I(G2,G1) < β
and |G1| |G2| and there is only one match (matching event) between G1 and all
groups in the next time window Ti+1
Splitting: I(G1,G2) < α and I(G2,G1) β and |G1| |G2| and there is more than one
match (matching events) between G2 and all groups in the previous time window Ti
Merging: I(G1,G2) α and I(G2,G1) < β and |G1| |G2| and there is more than one
match (matching events) between G1 and all groups in the next time window Ti+1
Dissolving: for G1 in Ti and each group G2 in Ti+1 I(G1,G2) < 10% and I(G2,G1) < 10%
Forming: for G2 in Ti+1 and each group G1 in Ti I(G1,G2) < 10% and I(G2,G1) < 10%

17. GED - Group Evolution
Discovery Method
Input: TSN in which at each timeframe Ti groups are extracted by any community detection
algorithm. Calculated any user importance measure.
For each pair of groups <G1, G2> in consecutive timeframes Ti and Ti+1 inclusion of G1 in G2
and G2 in G1 is counted according to equations (3).
Based on inclusion and size of two groups one type of event may be assigned:
Continuing: I(G1,G2) α and I(G2,G1) β and |G1| = |G2|
Shrinking: I(G1,G2) α and I(G2,G1) β and |G1| > |G2| OR I(G1,G2) < α and I(G2,G1)
β and |G1| |G2| and there is only one match (matching event) between G2 and all
groups in the previous time window Ti
Growing: I(G1,G2) α and I(G2,G1) β and |G1|<|G2| OR I(G1,G2) α and I(G2,G1) < β
and |G1| |G2| and there is only one match (matching event) between G1 and all
groups in the next time window Ti+1
Splitting: I(G1,G2) < α and I(G2,G1) β and |G1| |G2| and there is more than one
match (matching events) between G2 and all groups in the previous time window Ti
Merging: I(G1,G2) α and I(G2,G1) < β and |G1| |G2| and there is more than one
match (matching events) between G1 and all groups in the next time window Ti+1
Dissolving: for G1 in Ti and each group G2 in Ti+1 I(G1,G2) < 10% and I(G2,G1) < 10%
Forming: for G2 in Ti+1 and each group G1 in Ti I(G1,G2) < 10% and I(G2,G1) < 10%

18. GED: Following Events

19. Experiments: Setup
• Data Set
– Staff email exchange from WrUT (270K+ emails,2 years)
– 5,845 nodes and 149,344 edges
– Fourteen moving 90-days frames (overlap 45 days)
• Community extraction methods
– Fast modularity optimization (disjoint groups)
– CPM (overlapping groups)
• Methods for tracking group evolution
– by Asur et al.
– by Palla et al.
– GED

20. Experiments: Results
• Execution time
– Asur ~5.5h
– Palla ~7 days
– GED ~4h
• Group extraction method
– Palla works only with CPM
– Asur and GED work with any group extraction
method

21. Experiments: Results
• Palla returns all possible events between
groups, but does not assign its type
• Asur does not return all events and
sometimes assigns many events
(overlapping groups)
• GED may return all events depending on
and (near to 0) and assigns the event
type

22. Final Remarks
• Identification of event types for
group evolution
• Inclusion measure used for event
discovery
• Group Evolution Discovery (GED)
– a new method

23. Future Work: Event Prediction

24. Thank you for your attention 

25. Basic concepts:
Social position
Aktor SP Rank SP CD Rank CD
A 0,566 4 2 4
B 0,667 5 3 2
C 1,440 1 4 1
D 1,217 2 2 4
E 1,110 3 3 2