48,921 research outputs found
Mapping the Evolution of "Clusters": A Meta-analysis
This paper presents a meta-analysis of the âcluster literatureâ contained in scientific journals from 1969 to 2007. Thanks to an original database we study the evolution of a stream of literature which focuses on a research object which is both a theoretical puzzle and an empirical widespread evidence. We identify different growth stages, from take-off to development and maturity. We test the existence of a life-cycle within the authorships and we discover the existence of a substitutability relation between different collaborative behaviours. We study the relationships between a âspatialâ and an âindustrialâ approach within the textual corpus of cluster literature and we show the existence of a âpredatoryâ interaction. We detect the relevance of clustering behaviours in the location of authors working on clusters and in measuring the influence of geographical distance in co-authorship. We measure the extent of a convergence process of the vocabulary of scientists working on clusters.Cluster, Life-Cycle, Cluster Literature, Textual Analysis, Agglomeration, Co-Authorship
Sharp transition towards shared vocabularies in multi-agent systems
What processes can explain how very large populations are able to converge on
the use of a particular word or grammatical construction without global
coordination? Answering this question helps to understand why new language
constructs usually propagate along an S-shaped curve with a rather sudden
transition towards global agreement. It also helps to analyze and design new
technologies that support or orchestrate self-organizing communication systems,
such as recent social tagging systems for the web. The article introduces and
studies a microscopic model of communicating autonomous agents performing
language games without any central control. We show that the system undergoes a
disorder/order transition, going trough a sharp symmetry breaking process to
reach a shared set of conventions. Before the transition, the system builds up
non-trivial scale-invariant correlations, for instance in the distribution of
competing synonyms, which display a Zipf-like law. These correlations make the
system ready for the transition towards shared conventions, which, observed on
the time-scale of collective behaviors, becomes sharper and sharper with system
size. This surprising result not only explains why human language can scale up
to very large populations but also suggests ways to optimize artificial
semiotic dynamics.Comment: 12 pages, 4 figure
Biomedical Terminologies and Ontologies: Enabling Biomedical Semantic Interoperability and Standards in Europe
In the management of biomedical data, vocabularies such as ontologies and terminologies (O/Ts) are used for (i) domain knowledge representation and (ii) interoperability. The knowledge representation role supports the automated reasoning on, and analysis of, data annotated with O/Ts. At an interoperability level, the use of a communal vocabulary standard for a particular domain is essential for large data repositories and information management systems to communicate consistently with one other. Consequently, the interoperability benefit of selecting a particular O/T as a standard for data exchange purposes is often seen by the end-user as a function of the number of applications using that vocabulary (and, by extension, the size of the user base). Furthermore, the adoption of an O/T as an interoperability standard requires confidence in its stability and guaranteed continuity as a resource
The Naming Game in Social Networks: Community Formation and Consensus Engineering
We study the dynamics of the Naming Game [Baronchelli et al., (2006) J. Stat.
Mech.: Theory Exp. P06014] in empirical social networks. This stylized
agent-based model captures essential features of agreement dynamics in a
network of autonomous agents, corresponding to the development of shared
classification schemes in a network of artificial agents or opinion spreading
and social dynamics in social networks. Our study focuses on the impact that
communities in the underlying social graphs have on the outcome of the
agreement process. We find that networks with strong community structure hinder
the system from reaching global agreement; the evolution of the Naming Game in
these networks maintains clusters of coexisting opinions indefinitely. Further,
we investigate agent-based network strategies to facilitate convergence to
global consensus.Comment: The original publication is available at
http://www.springerlink.com/content/70370l311m1u0ng3
The Effect of Collective Attention on Controversial Debates on Social Media
We study the evolution of long-lived controversial debates as manifested on
Twitter from 2011 to 2016. Specifically, we explore how the structure of
interactions and content of discussion varies with the level of collective
attention, as evidenced by the number of users discussing a topic. Spikes in
the volume of users typically correspond to external events that increase the
public attention on the topic -- as, for instance, discussions about `gun
control' often erupt after a mass shooting.
This work is the first to study the dynamic evolution of polarized online
debates at such scale. By employing a wide array of network and content
analysis measures, we find consistent evidence that increased collective
attention is associated with increased network polarization and network
concentration within each side of the debate; and overall more uniform lexicon
usage across all users.Comment: accepted at ACM WebScience 201
A Trio Neural Model for Dynamic Entity Relatedness Ranking
Measuring entity relatedness is a fundamental task for many natural language
processing and information retrieval applications. Prior work often studies
entity relatedness in static settings and an unsupervised manner. However,
entities in real-world are often involved in many different relationships,
consequently entity-relations are very dynamic over time. In this work, we
propose a neural networkbased approach for dynamic entity relatedness,
leveraging the collective attention as supervision. Our model is capable of
learning rich and different entity representations in a joint framework.
Through extensive experiments on large-scale datasets, we demonstrate that our
method achieves better results than competitive baselines.Comment: In Proceedings of CoNLL 201
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