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MCD: A Modified Community Diversity Approach for Detecting Influential Nodes in Social Networks
Over the last couple of decades, Social Networks have connected people on the
web from across the globe and have become a crucial part of our daily life.
These networks have also rapidly grown as platforms for propagating products,
ideas, and opinions to target a wider audience. This calls for the need to find
influential nodes in a network for a variety of reasons, including the curb of
misinformation being spread across the networks, advertising products
efficiently, finding prominent protein structures in biological networks, etc.
In this paper, we propose Modified Community Diversity (MCD), a novel method
for finding influential nodes in a network by exploiting community detection
and a modified community diversity approach. We extend the concept of community
diversity to a two-hop scenario. This helps us evaluate a node's possible
influence over a network more accurately and also avoids the selection of seed
nodes with an overlapping scope of influence. Experimental results verify that
MCD outperforms various other state-of-the-art approaches on eight datasets
cumulatively across three performance metrics.Comment: 18 pages 4 Figure
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