Spread of Information in a Social Network Using Influential Nodes
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Transcript of Spread of Information in a Social Network Using Influential Nodes
Spread of Information in a
Social Network Using
Influential Nodes
Arpan Chaudhury
Partha Basuchowdhuri
Subhashis Majumder
(Heritage Institute of
Technology, Kolkata)
Introduction A social network is a set of actors
that may have relationships with one another.
It can be further described as the graph of relationships and interactions between a set of individuals.
Nodes are the individuals (visitors) within the networks, and ties are the different types of relationships between the individuals.
Social Networks
In a social network
represents a node
or a person
represents an edge
or a relationship
Our Aim
To find the set
of the most
“influential” such
that we can
maximize the
spread of
information
through those
“influentials”
Where to use….
-Viral marketing
-Word-of-mouth
marketing
strategies
Strategies to stop-
-Telecom churn
-Disease spread
Motivation behind our approach
- lesser the degree
of a node, the
correlation plot
shows that if we
choose that node as
that node as the
only seed to start
the spread of
information in the
network, more the
# of hops it takes
Why MST (Max. Spanning Tree) ?
Path through which information is more likely to flow
Identify nodes with high spread potential
Identify bridges passing information from one group to another
Remove insignificant edges retaining all nodes
0
4
2
1
5
6
7
3
8
3.5
2.5
4.5
3
44
4.5
3.54
2.5
2.53
3.5
D(0)=4D(1)=1
D(2)=2
D(3)=5D(4)=3
D(7)=3
D(5)=3
D(6)=4
D(8)=1
w(x) denotes weight of an edge x.
c(x) denotes cost of an edge x.
0.4
0.33
0.250.22
Max. Spanning Tree of the Network
Weight of the EdgesDegree of the NodesA Network
Edges in maximum spanning tree of the network represent the most
probable path of information flow
0.25
0.25
0.4
0.22
Modifying the graph to build MST
)(
1)(
2
)()()(
ij
ij
ij
eweC
jDiDew
Information Flow Path
Influential
Node
Information
Flow Path
Algorithm – Part I
Algorithm – Part II
Experimental Results
Dolphin Network
Dolphin network
with 62 vertices and
159 edges.
Popularly used as
benchmark data for
community
detection algorithms
in SNA.
Dolphin Network
Maximum spanning
tree of dolphin
network.
Dolphin Network
Core of dolphin
network, with k=7
AS Relationship Network
AS relation network
with 6474 vertices
and 13895 edges
from CAIDA.
Used here to test
our algorithm for a
large dataset
Maximum spanning
tree (MST) of
AS relationship
network.
AS Relationship Network
Core of AS
relationship network,
with k=18 (dth=50)
AS Relationship Network
Greedy k-center Vs. core-finding
Green/ cyan – first hop, Yellow – second hop,
Blue – third hop, Pink – fourth hop, White – fifth hop
Comparative study
Hop by hop spread comparison
Hop by hop spread comparison
Conclusion
Efficient and accurate compared to k-center.
Spread is simplistic and not community based,
hence takes very less time.
Work has been updated based on degree
discount and the model has been generalized
according to independent cascade model (all
edges won’t lead to spread).
Thank You