17,176 research outputs found
How to Network in Online Social Networks
In this paper, we consider how to maximize users' influence in Online Social
Networks (OSNs) by exploiting social relationships only. Our first contribution
is to extend to OSNs the model of Kempe et al. [1] on the propagation of
information in a social network and to show that a greedy algorithm is a good
approximation of the optimal algorithm that is NP-hard. However, the greedy
algorithm requires global knowledge, which is hardly practical. Our second
contribution is to show on simulations on the full Twitter social graph that
simple and practical strategies perform close to the greedy algorithm.Comment: NetSciCom 2014 - The Sixth IEEE International Workshop on Network
Science for Communication Networks (2014
Deciphering Network Community Structure by Surprise
The analysis of complex networks permeates all sciences, from biology to
sociology. A fundamental, unsolved problem is how to characterize the community
structure of a network. Here, using both standard and novel benchmarks, we show
that maximization of a simple global parameter, which we call Surprise (S),
leads to a very efficient characterization of the community structure of
complex synthetic networks. Particularly, S qualitatively outperforms the most
commonly used criterion to define communities, Newman and Girvan's modularity
(Q). Applying S maximization to real networks often provides natural,
well-supported partitions, but also sometimes counterintuitive solutions that
expose the limitations of our previous knowledge. These results indicate that
it is possible to define an effective global criterion for community structure
and open new routes for the understanding of complex networks.Comment: 7 pages, 5 figure
Online Influence Maximization (Extended Version)
Social networks are commonly used for marketing purposes. For example, free
samples of a product can be given to a few influential social network users (or
"seed nodes"), with the hope that they will convince their friends to buy it.
One way to formalize marketers' objective is through influence maximization (or
IM), whose goal is to find the best seed nodes to activate under a fixed
budget, so that the number of people who get influenced in the end is
maximized. Recent solutions to IM rely on the influence probability that a user
influences another one. However, this probability information may be
unavailable or incomplete. In this paper, we study IM in the absence of
complete information on influence probability. We call this problem Online
Influence Maximization (OIM) since we learn influence probabilities at the same
time we run influence campaigns. To solve OIM, we propose a multiple-trial
approach, where (1) some seed nodes are selected based on existing influence
information; (2) an influence campaign is started with these seed nodes; and
(3) users' feedback is used to update influence information. We adopt the
Explore-Exploit strategy, which can select seed nodes using either the current
influence probability estimation (exploit), or the confidence bound on the
estimation (explore). Any existing IM algorithm can be used in this framework.
We also develop an incremental algorithm that can significantly reduce the
overhead of handling users' feedback information. Our experiments show that our
solution is more effective than traditional IM methods on the partial
information.Comment: 13 pages. To appear in KDD 2015. Extended versio
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
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