Complex networks and data science provide an effective approach for understanding complex phenomena. Analysis of real-world network data has revealed universal patterns in social systems, such as lognormal group size distributions and strength of connections growing with number of connections. Computational social science combines network analysis, large data, and modeling to determine principles governing collective behavior and emergence in areas like information spreading, knowledge building, and epidemics. This interdisciplinary approach offers insights not available from isolated disciplines.

1. Complex networks and data science –
effective approach for understanding
complex phenomena
Marija Mitrović Dankulov
Institute of Physics Belgrade
University of Belgrade

2. Institute of Physics Belgrade

3. Scientific Computing Laboratory

4. Data Science and AI
- AI is altering the world: recommendation algorithms, image recognition,
health, autonomouse driving, knowledge organization and building, etc.
- AI is raising questions: etics, biases, misuse and abuse
- AI is strongly data dependent

5. AI is powerful, but …
- it is not infallible
- it is a black box
- it does not provide an insights about the mechanisms
- combination of AI with traditional approaches may be more reliable

6. Computational social science
Computational Social Science (CSS) - a new discipline that can offer:
- abstracted (simplified, idealized) models and methods (mainly from Statistical
Physics)
- large storage, algorithms and computational power (Computer and Data
Science)
- and a conceptual framework for the results to be interpreted (Social Science)

7. Socio-economic systems
Socio-economical system - large number of interacting entities that exhibits emergent
collective behavior

8. Social systems and collective behavior
- Social systems are complex system: consist of large number of interacting entities
- Social systems exhibit collective behavior: collective knowledge building, epidemic,
information spreading, etc.
- Social interactions involve individual’s actions and their attributes
- GOAL: to determine and understand the principals that govern the emergence of
collective behavior in social systems.
- MEANS:
- Methods and measures to quantify collective behavior in empirical data.
- Theoretical models that capture the dynamics of different social systems and
emergence of collective behavior.

9. Universality
- Universality - patterns are the same for different systems and different time periods
- Universality is observed in social systems - voting and turnout rates, citations,
mobility patterns, stocks, history, specific properties of biological and social networks,
etc.
- Observed patterns do not depend on specific details of each element, but rather on
macroscopic rules, forces that drive systems dynamics, and the structure of
interaction network

10. Central role of a social network
- Network of interactions is in the center of social systems: node - entities; edges -
interactions between entities
- The structure of a network is neither random nor regular - complex networks
- Structure of a complex networks and dynamics and function of the system are
inseparable connected
- They constantly evolve and change

11. Complex networks are everywhere

12. User networks: mapping
U - network of social interactions
Nodes: users
Links/Edges: direct interactions,
memberships, participations
Network types: directed/undirected,
binary/weighted

13. Bipartite networks: mapping
U + Q/A/C - bipartite network of techno-social interactions
Two partitions: U and Q/A/C
Network types: directed/undirected,
binary/weighted
Structural properties of: bipartite
networks and their projections to U/Q
partition

14. Systems and data
- Meetup: groups in London and New York; 4 groups - GEAM (Food&Drinks),
PGHF (Socializing), TECH (Tech), LVHK (Outdores & adventure)
- Reddit - subreddits
- StackExchange - active and closed communities on astronomy, physics,
literature and economics topic

15. Universal emergence of networks in Meetup groups
- Strentgth of users connections
grows with number of connections in
a similar for all four communities
- Memebers first expand their network
of connections and then strengthen
their connections
Source: J. Smiljanić, et al., PloS one 12,
e0171565 (2017)

16. Universal growth of social groups
- Lognormal distribution of
group sizes
- Stable distribution over time
and locations
- Different values of parameters
for different systems
Source: A. Vanić, et al., JSTAT 12,
e0171565 (2017)

17. Model of social group growth
Different importance of social linking - different distribution

18. Local cohesiveness - indicator of sustainability
- Sustainable groups have
higher local cohesiveness
across disciplines
- Early evolution of local
cohesivness is an early
indicator of sustainability
Source: Source: A. Vanić, et al.,
JSTAT 12, e0171565 (2017)

19. Summary
- Complex networks theory provides a powerful framework for studying
structure and dynamics of socio-economic systems
- Universal patterns are one of the prominent features socio-economic systems

20. Contact
Dr. Marija Mitrović Dankulov
Associate Research Professor and Head of Innovation Center
Institute of Physics Belgrade
National Competence Center Serbia for HPC/HPDA/AI
email: mitrovic@ipb.ac.rs
www.ipb.ac.rs, www.scl.rs