Discovery of digital communities in scientific platforms using network analysis.

1. Science 2.0 and engagement
Enrique Aldana, Michael Zentner, Sabine
Brunswicker, Satyam , Gerhard Klimeck
Source: cdn.strategyzer.com

2. 2
Science 2.0 is ...
Open Science
Tools
Open
Reproducible
Research
Source: strategyzer.com
Open access
Open Data
Close access,
data, tools
From Close to Open Science!

3. 3
Who has invested in Science 2.0?
Source: strategyzer.comSource: strategyzer.com
2007 - 2013
2014-2015
2003
US invested $100
million to open
access
EU invested €570
million e-
infrastructures
EU invested €170
million e-
infrastructures
Academia Industries & Institutions Public

4. 4
Current challenge for the Science 2.0 platforms
Source: strategyzer.com
Source: The Center for Open Science
Different platforms contribute
differently to science but ...
All are affected by the
resistance of engagement
and adoption of science 2.0
practices according to a
survey from European
Commission (2015)

5. 5
How engagement has been studied?
Behavioral
EngagementfactorsPlatformCommunity
Cognitive
Emotional
Qualitative Quantitative
Typeof
studies
Cognitive
Emotional
Qualitative
Participation behavior
Lurking behavior
Network behavior (?)
Networks have help to understand human
behaviors in the past, why not here?

6. 6
Different network metric explain phenomenon but how
can these metrics explain engagement?
How SN could help to the engagement problem?
Source: Center for
Management and
Organization Effectiveness
(CMOE)
Graph Images from: Wikipedia, Centrality
Diffusion of
Information
problem
Scientific Community
Influence
problem
Engagement
Representation of
Scientist Universe
Phenomenon Network metrics
Betweenness
centrality
Graph
representation
Betweenness
centrality
???

7. 7
Research Questions
- Do network metrics can explain engagement phenomena?
- Does profiling users by their log behavior affects network effects?
1
2

8. 8
Network metrics
Network
variables
If certain people do not
exist, do the news will be
spread over all the
network?
If you want to sell a
product by
recommendation. Who is
the most popular person in
the network?
If you have a party and
you uninvite someone,
who affects less the
number of guess?
Betweenness
Closeness
K-core

9. 9
RQ1: Network metric effects on engagement
Goal: Identify network variables
with effect to explain log
behavior
Number of team
when creating files
+
Max Consecutive
Activity duration
- Centrality measures:
● Degree
● Betweenness
● Closeness
- Coreness index
- Authority index
- Median degree of
Neighbors
- Network Community
size
Net vars
Control
variables
Linear Model:
Log behavior =
Adding # of Team
had low effect
Adding Degree
centrality had greater
effect than number of
team
Note:
- Model 1 = Dependant variable equal a constant
- Model 2 = Model 1 + new variable and so on...

10. 10
RQ2: Profiling users by log behavior
By using a pairwise non-parametric distribution test using cumulative distribution
functions, the Users were segmented by login’s behavior.
Empirical Cumulative Distribution Function for Top 10
users
0 5 10 15 20 25
(Thousand log during the users’ lifespan)
Users’ Profile
Visitor
Lurker
Persistant
Hard
working
1.0
0.8
0.6
0.4
0.2
0.0
LoginDistributionfunction
Users’ Profiles
Visitor
Lurker
Persistant
Hard
Working
TotalUser’sLogins

11. 11
Full model considering log behavior profiling
Profiles Net
Metric
Degree Betweeness Closeness Coreness
Visitor
Lurker
Persistant
Hard Worker
Profiles
Network Variables
• It’s a set of Network parameter in addition with
the profiling process that enables the
understanding of different user profiles
Significant on the full linear model

12. 12
Conclusions
• RQ1: Not all the network variables after in the same proportion to
the scientific population
• RQ2: Current metrics do not explain all different types of users
Future work:
• Consider transferability on different networks

13. 13
Thank you!!

14. 14
Metrics used in previous studies
1.Activity ratio: days executing something/ total days of life
2.Relative activity duration: number of days linked to the project/
total days of project
3.Variation in periodicity: Standard deviation of days between
sequential active days
4.Lurking ratio: number of days where entered to the platform but
does not contribute
5.Daily devoted time: no reliable information on the login duration
that could be extracted from the log files
Active
Absent
Lurking
Time between active days

15. 15
Community of Science 2.0 and its Engagement
[1] Oxford English Dictionary, http://www.oed.com/view/Entry/37337?redirectedFrom=community#eid, retrieved 08/12/17
Source: Center for
Management and
Organization Effectiveness
(CMOE)
Ant Community
Source:
projectmanagementfundas.blogs
pot.com
Geese Community
Less Complex Organizational Structure
High Complex
Scientific Community
Source: 123RF.com
The nature of the communities varies among objectives and groups

16. 16
Profiling Methodology from previous studies
1.Engagement metrics: Activity ratio, Relative Act, Lurking,
periodicity
2.Normalize engagement metrics
3.Segmentation between visitors and active members
> 2
days
Hierarchical
Clustering with
4 variables
Hierarchical
Clustering
without
periodicity
Validation of
clusters using
Davies-Bouldin
index and
Average
Silhouette
User K means
with the
number of
clusters found
Survey
Conclusions

17. 17
Clustering Methodology
1.Engagement metrics: Activity ratio, Relative
Act, Lurking, periodicity
2.Normalize engagement metrics
3.Segmentation between visitors and active
members
Hierarchical
Clustering with
4 variables
Hierarchical
Clustering
without
periodicity
Validation of
clusters using
Davies-Bouldin
index and
Average
Silhouette
User K means
with the
number of
clusters found
Survey
Conclusions
Validation of
clusters using
Davies-Bouldin
index and
Average
Silhouette
Anderson Darling
clustering

18. 18
Cluster validation
• External Index: Used to measure the extent to which cluster
labels match externally supplied class labels.
• Entropy Internal Index: Used to measure the goodness of a
clustering structure without respect to external information. Sum
of Squared Error (SSE)
• Relative Index: Used to compare two different clusterings or
clusters. Often an external or internal index is used for this
function, e.g., SSE or entropy

19. 19
Future work
1.Create the matrix of p-values for the anderson darling
2.Replace values >.05 as 1 and vales <.05
3.Create a graph using adjacency matrix

20. 20
Attributes that drives adoption and
diffusion of Scientific Tools
Community-Driven:
Is the community
interested to maintain
the tool?Trialability:
Is the people
willing to spend
time on the
tool?
Needs-Driven:
Is the tool really
solving someone
else’s problem?
Source: PHDCOMICS.com