126,240 research outputs found
Dynamic Animations of Journal Maps: Indicators of Structural Changes and Interdisciplinary Developments
The dynamic analysis of structural change in the organization of the sciences
requires methodologically the integration of multivariate and time-series
analysis. Structural change--e.g., interdisciplinary development--is often an
objective of government interventions. Recent developments in multi-dimensional
scaling (MDS) enable us to distinguish the stress originating in each
time-slice from the stress originating from the sequencing of time-slices, and
thus to locally optimize the trade-offs between these two sources of variance
in the animation. Furthermore, visualization programs like Pajek and Visone
allow us to show not only the positions of the nodes, but also their relational
attributes like betweenness centrality. Betweenness centrality in the vector
space can be considered as an indicator of interdisciplinarity. Using this
indicator, the dynamics of the citation impact environments of the journals
Cognitive Science, Social Networks, and Nanotechnology are animated and
assessed in terms of interdisciplinarity among the disciplines involved
Fazi relacijske jednačine i nejednačine i njihova primena u analizi podataka
The subject of this thesis is the development of algorithms for
computing the greatest solutions to systems of fuzzy relational
equations and inequalities and application of these solutions in the
analysis of one-mode and multi-mode fuzzy social networks. In
addition, some problems of finding structural similarities (regular
equivalences) between the actors of various networks have been
considered, and have been employed for determination of connected
positions in these networks
Analysis of Neighbourhoods in Multi-layered Dynamic Social Networks
Social networks existing among employees, customers or users of various IT
systems have become one of the research areas of growing importance. A social
network consists of nodes - social entities and edges linking pairs of nodes.
In regular, one-layered social networks, two nodes - i.e. people are connected
with a single edge whereas in the multi-layered social networks, there may be
many links of different types for a pair of nodes. Nowadays data about people
and their interactions, which exists in all social media, provides information
about many different types of relationships within one network. Analysing this
data one can obtain knowledge not only about the structure and characteristics
of the network but also gain understanding about semantic of human relations.
Are they direct or not? Do people tend to sustain single or multiple relations
with a given person? What types of communication is the most important for
them? Answers to these and more questions enable us to draw conclusions about
semantic of human interactions. Unfortunately, most of the methods used for
social network analysis (SNA) may be applied only to one-layered social
networks. Thus, some new structural measures for multi-layered social networks
are proposed in the paper, in particular: cross-layer clustering coefficient,
cross-layer degree centrality and various versions of multi-layered degree
centralities. Authors also investigated the dynamics of multi-layered
neighbourhood for five different layers within the social network. The
evaluation of the presented concepts on the real-world dataset is presented.
The measures proposed in the paper may directly be used to various methods for
collective classification, in which nodes are assigned to labels according to
their structural input features.Comment: 16 pages, International Journal of Computational Intelligence System
Multirelational Organization of Large-scale Social Networks in an Online World
The capacity to collect fingerprints of individuals in online media has
revolutionized the way researchers explore human society. Social systems can be
seen as a non-linear superposition of a multitude of complex social networks,
where nodes represent individuals and links capture a variety of different
social relations. Much emphasis has been put on the network topology of social
interactions, however, the multi-dimensional nature of these interactions has
largely been ignored in empirical studies, mostly because of lack of data.
Here, for the first time, we analyze a complete, multi-relational, large social
network of a society consisting of the 300,000 odd players of a massive
multiplayer online game. We extract networks of six different types of
one-to-one interactions between the players. Three of them carry a positive
connotation (friendship, communication, trade), three a negative (enmity, armed
aggression, punishment). We first analyze these types of networks as separate
entities and find that negative interactions differ from positive interactions
by their lower reciprocity, weaker clustering and fatter-tail degree
distribution. We then proceed to explore how the inter-dependence of different
network types determines the organization of the social system. In particular
we study correlations and overlap between different types of links and
demonstrate the tendency of individuals to play different roles in different
networks. As a demonstration of the power of the approach we present the first
empirical large-scale verification of the long-standing structural balance
theory, by focusing on the specific multiplex network of friendship and enmity
relations.Comment: 7 pages, 5 figures, accepted for publication in PNA
DeepWalk: Online Learning of Social Representations
We present DeepWalk, a novel approach for learning latent representations of
vertices in a network. These latent representations encode social relations in
a continuous vector space, which is easily exploited by statistical models.
DeepWalk generalizes recent advancements in language modeling and unsupervised
feature learning (or deep learning) from sequences of words to graphs. DeepWalk
uses local information obtained from truncated random walks to learn latent
representations by treating walks as the equivalent of sentences. We
demonstrate DeepWalk's latent representations on several multi-label network
classification tasks for social networks such as BlogCatalog, Flickr, and
YouTube. Our results show that DeepWalk outperforms challenging baselines which
are allowed a global view of the network, especially in the presence of missing
information. DeepWalk's representations can provide scores up to 10%
higher than competing methods when labeled data is sparse. In some experiments,
DeepWalk's representations are able to outperform all baseline methods while
using 60% less training data. DeepWalk is also scalable. It is an online
learning algorithm which builds useful incremental results, and is trivially
parallelizable. These qualities make it suitable for a broad class of real
world applications such as network classification, and anomaly detection.Comment: 10 pages, 5 figures, 4 table
Rethinking the patient: using Burden of Treatment Theory to understand the changing dynamics of illness
<b>Background</b> In this article we outline Burden of Treatment Theory, a new model of the relationship between sick people, their social networks, and healthcare services. Health services face the challenge of growing populations with long-term and life-limiting conditions, they have responded to this by delegating to sick people and their networks routine work aimed at managing symptoms, and at retarding - and sometimes preventing - disease progression. This is the new proactive work of patient-hood for which patients are increasingly accountable: founded on ideas about self-care, self-empowerment, and self-actualization, and on new technologies and treatment modalities which can be shifted from the clinic into the community. These place new demands on sick people, which they may experience as burdens of treatment.<p></p>
<b>Discussion</b> As the burdens accumulate some patients are overwhelmed, and the consequences are likely to be poor healthcare outcomes for individual patients, increasing strain on caregivers, and rising demand and costs of healthcare services. In the face of these challenges we need to better understand the resources that patients draw upon as they respond to the demands of both burdens of illness and burdens of treatment, and the ways that resources interact with healthcare utilization.<p></p>
<b>Summary</b> Burden of Treatment Theory is oriented to understanding how capacity for action interacts with the work that stems from healthcare. Burden of Treatment Theory is a structural model that focuses on the work that patients and their networks do. It thus helps us understand variations in healthcare utilization and adherence in different healthcare settings and clinical contexts
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