8,079 research outputs found
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
Detecting Real-World Influence Through Twitter
In this paper, we investigate the issue of detecting the real-life influence
of people based on their Twitter account. We propose an overview of common
Twitter features used to characterize such accounts and their activity, and
show that these are inefficient in this context. In particular, retweets and
followers numbers, and Klout score are not relevant to our analysis. We thus
propose several Machine Learning approaches based on Natural Language
Processing and Social Network Analysis to label Twitter users as Influencers or
not. We also rank them according to a predicted influence level. Our proposals
are evaluated over the CLEF RepLab 2014 dataset, and outmatch state-of-the-art
ranking methods.Comment: 2nd European Network Intelligence Conference (ENIC), Sep 2015,
Karlskrona, Swede
Internal combustion engine sensor network analysis using graph modeling
In recent years there has been a rapid development in technologies for smart monitoring applied to many different areas (e.g. building automation, photovoltaic systems, etc.). An intelligent monitoring system employs multiple sensors distributed within a network to extract useful information for decision-making. The management and the analysis of the raw data derived from the sensor network includes a number of specific challenges still unresolved, related to the different communication standards, the heterogeneous structure and the huge volume of data.
In this paper we propose to apply a method based on complex network theory, to evaluate the performance of an Internal Combustion Engine. Data are gathered from the OBD sensor subset and from the emission analyzer. The method provides for the graph modeling of the sensor network, where the nodes are represented by the sensors and the edge are evaluated with non-linear statistical correlation functions applied to the time series pairs.
The resulting functional graph is then analyzed with the topological metrics of the network, to define characteristic proprieties representing useful indicator for the maintenance and diagnosis
Who will lead and who will follow: Identifying Influential Users in Online Social Networks - A Critical Review and Future Research Directions
Along with the explosive growth of the phenomenon Online Social Networks (OSN), identifying influential users in OSN has received a great deal of attention in recent years. However, the development of practical approaches for identifying them is still in its infancy. By means of a structured literature review, the authors analyze and synthesize the publications particularly from two perspectives. From a research perspective, they find that existing approaches mostly build on users’ connectivity and activity but hardly consider further characteristics of influential users. Moreover, they outline two major research streams. It becomes apparent that most marketing-oriented articles draw on real-world data of OSN, while more technology-oriented papers rather have a theoretical approach and mostly evaluate their artifacts by means of formal proofs. The authors find that a stronger collaboration between the scientific Business and Information Systems Engineering (BISE) and Marketing communities could be mutually beneficial. With respect to a practitioner’s perspective, they compile advice on the practical application of approaches for the identification of influential users. It is hoped that the results can stimulate and guide future research
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