153,863 research outputs found
Predicting Social Links for New Users across Aligned Heterogeneous Social Networks
Online social networks have gained great success in recent years and many of
them involve multiple kinds of nodes and complex relationships. Among these
relationships, social links among users are of great importance. Many existing
link prediction methods focus on predicting social links that will appear in
the future among all users based upon a snapshot of the social network. In
real-world social networks, many new users are joining in the service every
day. Predicting links for new users are more important. Different from
conventional link prediction problems, link prediction for new users are more
challenging due to the following reasons: (1) differences in information
distributions between new users and the existing active users (i.e., old
users); (2) lack of information from the new users in the network. We propose a
link prediction method called SCAN-PS (Supervised Cross Aligned Networks link
prediction with Personalized Sampling), to solve the link prediction problem
for new users with information transferred from both the existing active users
in the target network and other source networks through aligned accounts. We
proposed a within-target-network personalized sampling method to process the
existing active users' information in order to accommodate the differences in
information distributions before the intra-network knowledge transfer. SCAN-PS
can also exploit information in other source networks, where the user accounts
are aligned with the target network. In this way, SCAN-PS could solve the cold
start problem when information of these new users is total absent in the target
network.Comment: 11 pages, 10 figures, 4 table
Dynamical patterns of epidemic outbreaks in complex heterogeneous networks
We present a thorough inspection of the dynamical behavior of epidemic
phenomena in populations with complex and heterogeneous connectivity patterns.
We show that the growth of the epidemic prevalence is virtually instantaneous
in all networks characterized by diverging degree fluctuations, independently
of the structure of the connectivity correlation functions characterizing the
population network. By means of analytical and numerical results, we show that
the outbreak time evolution follows a precise hierarchical dynamics. Once
reached the most highly connected hubs, the infection pervades the network in a
progressive cascade across smaller degree classes. Finally, we show the
influence of the initial conditions and the relevance of statistical results in
single case studies concerning heterogeneous networks. The emerging theoretical
framework appears of general interest in view of the recently observed
abundance of natural networks with complex topological features and might
provide useful insights for the development of adaptive strategies aimed at
epidemic containment.Comment: 13 pages, 11 figure
Social capital and rural innovation process. The evaluation of the measure 124 \u201cCooperation for Development of New Products, Processes and Technologies in the Agriculture, Food and Forestry Sector\u201d in the Umbria Region (Italy)
The most recent theories on innovation point out the role of social networks, demonstrating how knowledge is intertwined with network communities and social capital represents an essential factor to comprehend innovation. The social network dimension of the innovation process is also acknowledged in the actual definition of an agricultural innovation system (AIS). This study attempts to assess the role played by social capital in agricultural innovation projects co-financed by the Measure 124 of the Rural Development Program (2007-2013) of the Umbria Region (Italy), based on the analysis of 5 evaluation criteria (relevance, innovation, effectiveness, sustainability, and social capital) in relation to 8 selected projects. The obtained results confirm the validity of the proposed methodology both for the purpose of internal monitoring of the project and for the assessment of the measure on the basis of tangible and intangible factors, such as social capital
An integrated ranking algorithm for efficient information computing in social networks
Social networks have ensured the expanding disproportion between the face of
WWW stored traditionally in search engine repositories and the actual ever
changing face of Web. Exponential growth of web users and the ease with which
they can upload contents on web highlights the need of content controls on
material published on the web. As definition of search is changing,
socially-enhanced interactive search methodologies are the need of the hour.
Ranking is pivotal for efficient web search as the search performance mainly
depends upon the ranking results. In this paper new integrated ranking model
based on fused rank of web object based on popularity factor earned over only
valid interlinks from multiple social forums is proposed. This model identifies
relationships between web objects in separate social networks based on the
object inheritance graph. Experimental study indicates the effectiveness of
proposed Fusion based ranking algorithm in terms of better search results.Comment: 14 pages, International Journal on Web Service Computing (IJWSC),
Vol.3, No.1, March 201
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