3,459 research outputs found
Collective Influence of Multiple Spreaders Evaluated by Tracing Real Information Flow in Large-Scale Social Networks
Identifying the most influential spreaders that maximize information flow is
a central question in network theory. Recently, a scalable method called
"Collective Influence (CI)" has been put forward through collective influence
maximization. In contrast to heuristic methods evaluating nodes' significance
separately, CI method inspects the collective influence of multiple spreaders.
Despite that CI applies to the influence maximization problem in percolation
model, it is still important to examine its efficacy in realistic information
spreading. Here, we examine real-world information flow in various social and
scientific platforms including American Physical Society, Facebook, Twitter and
LiveJournal. Since empirical data cannot be directly mapped to ideal
multi-source spreading, we leverage the behavioral patterns of users extracted
from data to construct "virtual" information spreading processes. Our results
demonstrate that the set of spreaders selected by CI can induce larger scale of
information propagation. Moreover, local measures as the number of connections
or citations are not necessarily the deterministic factors of nodes' importance
in realistic information spreading. This result has significance for rankings
scientists in scientific networks like the APS, where the commonly used number
of citations can be a poor indicator of the collective influence of authors in
the community.Comment: 11 pages, 4 figure
Theories for influencer identification in complex networks
In social and biological systems, the structural heterogeneity of interaction
networks gives rise to the emergence of a small set of influential nodes, or
influencers, in a series of dynamical processes. Although much smaller than the
entire network, these influencers were observed to be able to shape the
collective dynamics of large populations in different contexts. As such, the
successful identification of influencers should have profound implications in
various real-world spreading dynamics such as viral marketing, epidemic
outbreaks and cascading failure. In this chapter, we first summarize the
centrality-based approach in finding single influencers in complex networks,
and then discuss the more complicated problem of locating multiple influencers
from a collective point of view. Progress rooted in collective influence
theory, belief-propagation and computer science will be presented. Finally, we
present some applications of influencer identification in diverse real-world
systems, including online social platforms, scientific publication, brain
networks and socioeconomic systems.Comment: 24 pages, 6 figure
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
- …