complex network analysis
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- 1. Complex Network Analysis
- 2. What will you get to know ?
To stop the fire you have to create fire
Why do your friends seem to be more popular than you are
Are we living in a Small World
How do we detect epidemics early
Friendship network in BITS
Behavior in Online Social Networking Sites
How popular is something on DC++ - 3. Complex Networks
Non-trivial real-life networks
Observed in most Social, Biological and Computer networks. - 4. The Friendship Paradox
On an average, your friends have more friends than you do
True for all networks (or graphs).
Prominent in real life networks. - 5. The Small World Phenomenon
Any two persons in the world are connected by at most six links of acquaintances.
Among Mathematicians: Erds Number (Paul Erds)
Among Actors: Bacon Number (Kevin Bacon) - 6. http://findthebacon.com/Play.aspx
- 7. Complex Network Analysis
Diameter: Then number of links in the shortest path between furthest nodes. (Small World)
Average path-length
Degree: Number of links on a particular node(Number of neighbors) - 8. Network Density: The ratio of edges in the network to the
max possible number of edges.
Density of a social network with large number of nodes is highly unlikely to exceed 0.5 - 9. Clustering Coefficient: Likelihood that two associates of a
node are associates themselves
Lies between 0 and 1
Y
X
A - 10. Centrality Measures (Betweenness): The number of shortest
path that passes through a node.
Synonymous with importance.
Important in study of spreading of forest fires, rumors, information, epidemics etc.
Revisit Friendship Paradox - 11. BITSian Friendship Network
- 12. BITSian Friendship Network
Network Density: 0.37
Diameter: 4
Average Path-length: 1.99
Average Clustering Coefficient: 0.51 - 13. Twitter Growth Model
With probability p, a new node(user) enters the network and links with one existing node.
With probability q = 1-p, an existing user gets linked to an existing node.
Preferential Selection:
P(deg i -> deg i+1) proportional to (i+constant) - 14. The Twitter growth model
The rate equations are: - 15. Formula vs Model Simulation
- 16. Model vs Twitter Data
- 17. Power Law!!!
Degree distribution: n(j) = c.j-
Straight line in log-log plot.
Scale free networks.
Many networks conjectured(and many found) to follow power law.
Eg.-Online Social Networks, Friendship Network, Collaboration Network (Movie-Actor, Research-Scientists), World Wide Web, Protien-Protien Interaction, Airline Networks
Pareto Principle: 80-20 rule. - 18. DC++ Search Spy
A similar approach can be applied to find out number of searches vs rank of search query.
query
keyword - 19. Power Law !!!
- 20. Rank of a keyword (node) = number of nodes with degree
greater than its degree.
The inverse function gives the frequency of a keyword ranked r:
POWER LAW !!! - 21. Formula matches with the Real DC++ data