1,225 research outputs found
Mitigating Misinformation Spreading in Social Networks Via Edge Blocking
The wide adoption of social media platforms has brought about numerous
benefits for communication and information sharing. However, it has also led to
the rapid spread of misinformation, causing significant harm to individuals,
communities, and society at large. Consequently, there has been a growing
interest in devising efficient and effective strategies to contain the spread
of misinformation. One popular countermeasure is blocking edges in the
underlying network.
We model the spread of misinformation using the classical Independent Cascade
model and study the problem of minimizing the spread by blocking a given number
of edges. We prove that this problem is computationally hard, but we propose an
intuitive community-based algorithm, which aims to detect well-connected
communities in the network and disconnect the inter-community edges. Our
experiments on various real-world social networks demonstrate that the proposed
algorithm significantly outperforms the prior methods, which mostly rely on
centrality measures
A Survey on Centrality Metrics and Their Implications in Network Resilience
Centrality metrics have been used in various networks, such as communication,
social, biological, geographic, or contact networks. In particular, they have
been used in order to study and analyze targeted attack behaviors and
investigated their effect on network resilience. Although a rich volume of
centrality metrics has been developed for decades, a limited set of centrality
metrics have been commonly in use. This paper aims to introduce various
existing centrality metrics and discuss their applicabilities and performance
based on the results obtained from extensive simulation experiments to
encourage their use in solving various computing and engineering problems in
networks.Comment: Main paper: 36 pages, 2 figures. Appendix 23 pages,45 figure
Separation Framework: An Enabler for Cooperative and D2D Communication for Future 5G Networks
Soaring capacity and coverage demands dictate that future cellular networks
need to soon migrate towards ultra-dense networks. However, network
densification comes with a host of challenges that include compromised energy
efficiency, complex interference management, cumbersome mobility management,
burdensome signaling overheads and higher backhaul costs. Interestingly, most
of the problems, that beleaguer network densification, stem from legacy
networks' one common feature i.e., tight coupling between the control and data
planes regardless of their degree of heterogeneity and cell density.
Consequently, in wake of 5G, control and data planes separation architecture
(SARC) has recently been conceived as a promising paradigm that has potential
to address most of aforementioned challenges. In this article, we review
various proposals that have been presented in literature so far to enable SARC.
More specifically, we analyze how and to what degree various SARC proposals
address the four main challenges in network densification namely: energy
efficiency, system level capacity maximization, interference management and
mobility management. We then focus on two salient features of future cellular
networks that have not yet been adapted in legacy networks at wide scale and
thus remain a hallmark of 5G, i.e., coordinated multipoint (CoMP), and
device-to-device (D2D) communications. After providing necessary background on
CoMP and D2D, we analyze how SARC can particularly act as a major enabler for
CoMP and D2D in context of 5G. This article thus serves as both a tutorial as
well as an up to date survey on SARC, CoMP and D2D. Most importantly, the
article provides an extensive outlook of challenges and opportunities that lie
at the crossroads of these three mutually entangled emerging technologies.Comment: 28 pages, 11 figures, IEEE Communications Surveys & Tutorials 201
Studying Fake News via Network Analysis: Detection and Mitigation
Social media for news consumption is becoming increasingly popular due to its
easy access, fast dissemination, and low cost. However, social media also
enable the wide propagation of "fake news", i.e., news with intentionally false
information. Fake news on social media poses significant negative societal
effects, and also presents unique challenges. To tackle the challenges, many
existing works exploit various features, from a network perspective, to detect
and mitigate fake news. In essence, news dissemination ecosystem involves three
dimensions on social media, i.e., a content dimension, a social dimension, and
a temporal dimension. In this chapter, we will review network properties for
studying fake news, introduce popular network types and how these networks can
be used to detect and mitigation fake news on social media.Comment: Submitted as a invited book chapter in Lecture Notes in Social
Networks, Springer Pres
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