67,217 research outputs found
Supervised Random Walks: Predicting and Recommending Links in Social Networks
Predicting the occurrence of links is a fundamental problem in networks. In
the link prediction problem we are given a snapshot of a network and would like
to infer which interactions among existing members are likely to occur in the
near future or which existing interactions are we missing. Although this
problem has been extensively studied, the challenge of how to effectively
combine the information from the network structure with rich node and edge
attribute data remains largely open.
We develop an algorithm based on Supervised Random Walks that naturally
combines the information from the network structure with node and edge level
attributes. We achieve this by using these attributes to guide a random walk on
the graph. We formulate a supervised learning task where the goal is to learn a
function that assigns strengths to edges in the network such that a random
walker is more likely to visit the nodes to which new links will be created in
the future. We develop an efficient training algorithm to directly learn the
edge strength estimation function.
Our experiments on the Facebook social graph and large collaboration networks
show that our approach outperforms state-of-the-art unsupervised approaches as
well as approaches that are based on feature extraction
Complete trails of co-authorship network evolution
The rise and fall of a research field is the cumulative outcome of its
intrinsic scientific value and social coordination among scientists. The
structure of the social component is quantifiable by the social network of
researchers linked via co-authorship relations, which can be tracked through
digital records. Here, we use such co-authorship data in theoretical physics
and study their complete evolutionary trail since inception, with a particular
emphasis on the early transient stages. We find that the co-authorship networks
evolve through three common major processes in time: the nucleation of small
isolated components, the formation of a tree-like giant component through
cluster aggregation, and the entanglement of the network by large-scale loops.
The giant component is constantly changing yet robust upon link degradations,
forming the network's dynamic core. The observed patterns are successfully
reproducible through a new network model
Opinion mining and sentiment analysis in marketing communications: a science mapping analysis in Web of Science (1998–2018)
Opinion mining and sentiment analysis has become ubiquitous in our society, with
applications in online searching, computer vision, image understanding, artificial intelligence and
marketing communications (MarCom). Within this context, opinion mining and sentiment analysis
in marketing communications (OMSAMC) has a strong role in the development of the field by
allowing us to understand whether people are satisfied or dissatisfied with our service or product
in order to subsequently analyze the strengths and weaknesses of those consumer experiences. To
the best of our knowledge, there is no science mapping analysis covering the research about opinion
mining and sentiment analysis in the MarCom ecosystem. In this study, we perform a science
mapping analysis on the OMSAMC research, in order to provide an overview of the scientific work
during the last two decades in this interdisciplinary area and to show trends that could be the basis
for future developments in the field. This study was carried out using VOSviewer, CitNetExplorer
and InCites based on results from Web of Science (WoS). The results of this analysis show the
evolution of the field, by highlighting the most notable authors, institutions, keywords,
publications, countries, categories and journals.The research was funded by Programa Operativo FEDER Andalucía 2014‐2020, grant number “La
reputación de las organizaciones en una sociedad digital. Elaboración de una Plataforma Inteligente para la
Localización, Identificación y Clasificación de Influenciadores en los Medios Sociales Digitales (UMA18‐
FEDERJA‐148)” and The APC was funded by the same research gran
A Multi-Relational Network to Support the Scholarly Communication Process
The general pupose of the scholarly communication process is to support the
creation and dissemination of ideas within the scientific community. At a finer
granularity, there exists multiple stages which, when confronted by a member of
the community, have different requirements and therefore different solutions.
In order to take a researcher's idea from an initial inspiration to a community
resource, the scholarly communication infrastructure may be required to 1)
provide a scientist initial seed ideas; 2) form a team of well suited
collaborators; 3) located the most appropriate venue to publish the formalized
idea; 4) determine the most appropriate peers to review the manuscript; and 5)
disseminate the end product to the most interested members of the community.
Through the various delinieations of this process, the requirements of each
stage are tied soley to the multi-functional resources of the community: its
researchers, its journals, and its manuscritps. It is within the collection of
these resources and their inherent relationships that the solutions to
scholarly communication are to be found. This paper describes an associative
network composed of multiple scholarly artifacts that can be used as a medium
for supporting the scholarly communication process.Comment: keywords: digital libraries and scholarly communicatio
The power of indirect social ties
While direct social ties have been intensely studied in the context of
computer-mediated social networks, indirect ties (e.g., friends of friends)
have seen little attention. Yet in real life, we often rely on friends of our
friends for recommendations (of good doctors, good schools, or good
babysitters), for introduction to a new job opportunity, and for many other
occasional needs. In this work we attempt to 1) quantify the strength of
indirect social ties, 2) validate it, and 3) empirically demonstrate its
usefulness for distributed applications on two examples. We quantify social
strength of indirect ties using a(ny) measure of the strength of the direct
ties that connect two people and the intuition provided by the sociology
literature. We validate the proposed metric experimentally by comparing
correlations with other direct social tie evaluators. We show via data-driven
experiments that the proposed metric for social strength can be used
successfully for social applications. Specifically, we show that it alleviates
known problems in friend-to-friend storage systems by addressing two previously
documented shortcomings: reduced set of storage candidates and data
availability correlations. We also show that it can be used for predicting the
effects of a social diffusion with an accuracy of up to 93.5%.Comment: Technical Repor
Scale-free networks with an exponent less than two
We study scale free simple graphs with an exponent of the degree distribution
less than two. Generically one expects such extremely skewed networks
-- which occur very frequently in systems of virtually or logically connected
units -- to have different properties than those of scale free networks with
: The number of links grows faster than the number of nodes and they
naturally posses the small world property, because the diameter increases by
the logarithm of the size of the network and the clustering coefficient is
finite. We discuss a simple prototype model of such networks, inspired by real
world phenomena, which exhibits these properties and allows for a detailed
analytical investigation
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