Background:
Detection of influential actors in social media such as Twitter or Facebook plays an important role for improving the quality and efficiency of work and services in many fields such as education and marketing.
Methods:
The work described here aims to introduce a new approach that characterizes the influence of actors by the strength of attracting new active members into a networked community. We present a model of influence of an actor that is based on the attractiveness of the actor in terms of the number of other new actors with which he or she has established relations over time.
Results:
We have used this concept and measure of influence to determine optimal seeds in a simulation of influence maximization using two empirically collected social networks for the underlying graphs.
Conclusions:
Our empirical results on the datasets demonstrate that our measure stands out as a useful measure to define the attractors comparing to the other influence measures.
Background:
Influential actors detection in social media such as twitter or Facebook can play a major role in gathering opinions on particular topics, improving the market
-
ing efficiency, predicting the trends, etc.
Proposed methods:
This work aims to extend our formally defined
T
measure to
present a new measure aiming to recognize the actor’s influence by the strength of
attracting new important actors into a networked community. Therefore, we propose a
model of the actor’s influence based on the attractiveness of the actor in relation to the
number of other attractors with whom he/she has established connections over time.
Results and conclusions:
Using an empirically collected social network for the
underlying graph, we have applied the above-mentioned measure of influence in
order to determine optimal seeds in a simulation of influence maximization. We study
our extended measure in the context of information diffusion because this measure is
based on a model of actors who attract others to be active members in a community.
This corresponds to the idea of the IC simulation model which is used to identify the
most important spreaders in a set of actors.
Keywords: Actor influence, Social media networks, Twitter, IC model, Information
diffusion, Independent cascade model, T measure
Technologie die beflügelt
(2016)
In recent years, the number of reasonable powerful mobile devices increased. In 2011, the number of smartphones(e.g.)increased to more than 300 million units. A lot of research has already been conducted with respect of mobile devices acting as Cloud Service consumers, but
still not much effort is put on mobile devices in the role of Cloud Service providers. Therefore, this paper presents an approach that allows to utilize mobile devices like smart phones or tablets as Cloud Service providers. In order to make this a reasonable approach, some of the occurring problems are discussed and it is shown how the presented architecture is able to overcome these problems. Last
but not least, this paper
describes some performance
tests of the chosen implementa
tion for mobile Web Services.
Der Einsatz von virtuellen Servern im LDS NRW erfolgte bisher unter dem Blickwinkel der Konsolidierung von einfachen und sehr einfachen Systemen, die keine dedizierte Serversystemtechnik benötigten.
Mittlerweile bietet VMware Funktionalitäten, die neben dem Konsolidierungsgedanken hoch interessante Möglichkeiten für vielfältigste, individuelle Kundenanforderungen bieten. Dies reicht von flexiblen, preiswerten und einfachen
Systemen bis hin zu Serverplattformen mit hohen Ansprüchen an Performance und Verfügbarkeit.
In this paper we present an approach for contextual big data analytics in social networks, particularly in Twitter. The combination of a Rich Context Model (RCM) with machine learning is used in order to improve the quality of the data mining techniques. We propose the algorithm and architecture of our approach for real-time contextual analysis of tweets. The proposed approach can be used to enrich and empower the predictive analytics or to provide relevant context-aware recommendations.
One of the latest hypes in IT is the well-known Cloud
Computing paradigm. This paradigm that showed up in recent years
is a paradigm for the dynamic usage of computational power, memory and other computational resources. With respect to hypes, the author strongly believes that the
Cloud Computing paradigm has the potential to survive the hype and to become a usual technology used for the provision of IT based services. Therefore, it will be necessary to deploy Cloud Computing based infrastructures in a professional, stable and reliable way. This would lead to the idea that the Cloud Computing paradigm needs to be concerned with respect to IT Service Management, since cloud based infrastructures have to be managed differently in comparison to a usual infrastructure. This paper discusses, based on the IT Infrastructure Library (ITIL), as the de-facto standard for IT Service Management, whether this de-facto standard might also be able to manage Cloud Computing based infrastructures, how the according processes might change and whether ITIL supports a division of labor between the customer and the service provider
of a Cloud Computing based infrastructure.