91 research outputs found
Intelligent Complex Networks
The present work addresses the issue of using complex networks as artificial
intelligence mechanisms. More specifically, we consider the situation in which
puzzles, represented as complex networks of varied types, are to be assembled
by complex network processing engines of diverse structures. The puzzle pieces
are initially distributed on a set of nodes chosen according to different
criteria, including degree and eigenvector centrality. The pieces are then
repeatedly copied to the neighboring nodes. The provision of buffering of
different sizes are also investigated. Several interesting results are
identified, including the fact that BA-based assembling engines tend to provide
the fastest solutions. It is also found that the distribution of pieces
according to the eigenvector centrality almost invariably leads to the best
performance. Another result is that using the buffer sizes proportional to the
degree of the respective nodes tend to improve the performance
How Integrated are Theoretical and Applied Physics?
How well integrated are more theoretically and application oriented works in
Physics currently? This interesting question, which has several relevant
implications, has been approached mostly in a more subjective way. Recent
concepts and methods from network science are used in the current work in order
to develop a more principled, quantitative and objective approach to gauging
the integration and centrality of more theoretical/applied journals within the
APS journals database, represented as a directed and undirected citation
network. The results suggest a surprising level of integration between more
theoretically and application oriented journals, which are also characterized
by remarkably similar centralities in the network
Topic segmentation via community detection in complex networks
Many real systems have been modelled in terms of network concepts, and
written texts are a particular example of information networks. In recent
years, the use of network methods to analyze language has allowed the discovery
of several interesting findings, including the proposition of novel models to
explain the emergence of fundamental universal patterns. While syntactical
networks, one of the most prevalent networked models of written texts, display
both scale-free and small-world properties, such representation fails in
capturing other textual features, such as the organization in topics or
subjects. In this context, we propose a novel network representation whose main
purpose is to capture the semantical relationships of words in a simple way. To
do so, we link all words co-occurring in the same semantic context, which is
defined in a threefold way. We show that the proposed representations favours
the emergence of communities of semantically related words, and this feature
may be used to identify relevant topics. The proposed methodology to detect
topics was applied to segment selected Wikipedia articles. We have found that,
in general, our methods outperform traditional bag-of-words representations,
which suggests that a high-level textual representation may be useful to study
semantical features of texts
Knowledge Acquisition: A Complex Networks Approach
Complex networks have been found to provide a good representation of the
structure of knowledge, as understood in terms of discoverable concepts and
their relationships. In this context, the discovery process can be modeled as
agents walking in a knowledge space. Recent studies proposed more realistic
dynamics, including the possibility of agents being influenced by others with
higher visibility or by their own memory. However, rather than dealing with
these two concepts separately, as previously approached, in this study we
propose a multi-agent random walk model for knowledge acquisition that
incorporates both concepts. More specifically, we employed the true self
avoiding walk alongside a new dynamics based on jumps, in which agents are
attracted by the influence of others. That was achieved by using a L\'evy
flight influenced by a field of attraction emanating from the agents. In order
to evaluate our approach, we use a set of network models and two real networks,
one generated from Wikipedia and another from the Web of Science. The results
were analyzed globally and by regions. In the global analysis, we found that
most of the dynamics parameters do not significantly affect the discovery
dynamics. The local analysis revealed a substantial difference of performance
depending on the network regions where the dynamics are occurring. In
particular, the dynamics at the core of networks tend to be more effective. The
choice of the dynamics parameters also had no significant impact to the
acquisition performance for the considered knowledge networks, even at the
local scale
Paragraph-based complex networks: application to document classification and authenticity verification
With the increasing number of texts made available on the Internet, many
applications have relied on text mining tools to tackle a diversity of
problems. A relevant model to represent texts is the so-called word adjacency
(co-occurrence) representation, which is known to capture mainly syntactical
features of texts.In this study, we introduce a novel network representation
that considers the semantic similarity between paragraphs. Two main properties
of paragraph networks are considered: (i) their ability to incorporate
characteristics that can discriminate real from artificial, shuffled
manuscripts and (ii) their ability to capture syntactical and semantic textual
features. Our results revealed that real texts are organized into communities,
which turned out to be an important feature for discriminating them from
artificial texts. Interestingly, we have also found that, differently from
traditional co-occurrence networks, the adopted representation is able to
capture semantic features. Additionally, the proposed framework was employed to
analyze the Voynich manuscript, which was found to be compatible with texts
written in natural languages. Taken together, our findings suggest that the
proposed methodology can be combined with traditional network models to improve
text classification tasks
How Coupled are Mass Spectrometry and Capillary Electrophoresis?
The understanding of how science works can contribute to making scientific
development more effective. In this paper, we report an analysis of the
organization and interconnection between two important issues in chemistry,
namely mass spectrometry (MS) and capillary electrophoresis (CE). For that
purpose, we employed science of science techniques based on complex networks.
More specifically, we considered a citation network in which the nodes and
connections represent papers and citations, respectively. Interesting results
were found, including a good separation between some clusters of articles
devoted to instrumentation techniques and applications. However, the papers
that describe CE-MS did not lead to a well-defined cluster. In order to better
understand the organization of the citation network, we considered a
multi-scale analysis, in which we used the information regarding sub-clusters.
Firstly, we analyzed the sub-cluster of the first article devoted to the
coupling between CE and MS, which was found to be a good representation of its
sub-cluster. The second analysis was about the sub-cluster of a seminal paper
known to be the first that dealt with proteins by using CE-MS. By considering
the proposed methodologies, our paper paves the way for researchers working
with both techniques, since it elucidates the knowledge organization and can
therefore lead to better literature reviews
An Image Analysis Approach to the Calligraphy of Books
Text network analysis has received increasing attention as a consequence of
its wide range of applications. In this work, we extend a previous work founded
on the study of topological features of mesoscopic networks. Here, the
geometrical properties of visualized networks are quantified in terms of
several image analysis techniques and used as subsidies for authorship
attribution. It was found that the visual features account for performance
similar to that achieved by using topological measurements. In addition, the
combination of these two types of features improved the performance
Connecting Network Science and Information Theory
A framework integrating information theory and network science is proposed,
giving rise to a potentially new area. By incorporating and integrating
concepts such as complexity, coding, topological projections and network
dynamics, the proposed network-based framework paves the way not only to
extending traditional information science, but also to modeling, characterizing
and analyzing a broad class of real-world problems, from language communication
to DNA coding. Basically, an original network is supposed to be transmitted,
with or without compaction, through a sequence of symbols or time-series
obtained by sampling its topology by some network dynamics, such as random
walks. We show that the degree of compression is ultimately related to the
ability to predict the frequency of symbols based on the topology of the
original network and the adopted dynamics. The potential of the proposed
approach is illustrated with respect to the efficiency of transmitting several
types of topologies by using a variety of random walks. Several interesting
results are obtained, including the behavior of the Barab\'asi-Albert model
oscillating between high and low performance depending on the considered
dynamics, and the distinct performances obtained for two geographical models
Representation of texts as complex networks: a mesoscopic approach
Statistical techniques that analyze texts, referred to as text analytics,
have departed from the use of simple word count statistics towards a new
paradigm. Text mining now hinges on a more sophisticated set of methods,
including the representations in terms of complex networks. While
well-established word-adjacency (co-occurrence) methods successfully grasp
syntactical features of written texts, they are unable to represent important
aspects of textual data, such as its topical structure, i.e. the sequence of
subjects developing at a mesoscopic level along the text. Such aspects are
often overlooked by current methodologies. In order to grasp the mesoscopic
characteristics of semantical content in written texts, we devised a network
model which is able to analyze documents in a multi-scale fashion. In the
proposed model, a limited amount of adjacent paragraphs are represented as
nodes, which are connected whenever they share a minimum semantical content. To
illustrate the capabilities of our model, we present, as a case example, a
qualitative analysis of "Alice's Adventures in Wonderland". We show that the
mesoscopic structure of a document, modeled as a network, reveals many semantic
traits of texts. Such an approach paves the way to a myriad of semantic-based
applications. In addition, our approach is illustrated in a machine learning
context, in which texts are classified among real texts and randomized
instances
The Dynamics of Knowledge Acquisition via Self-Learning in Complex Networks
Studies regarding knowledge organization and acquisition are of great
importance to understand areas related to science and technology. A common way
to model the relationship between different concepts is through complex
networks. In such representations, network's nodes store knowledge and edges
represent their relationships. Several studies that considered this type of
structure and knowledge acquisition dynamics employed one or more agents to
discover node concepts by walking on the network. In this study, we investigate
a different type of dynamics considering a single node as the "network brain".
Such brain represents a range of real systems such as the information about the
environment that is acquired by a person and is stored in the brain. To store
the discovered information in a specific node, the agents walk on the network
and return to the brain. We propose three different dynamics and test them on
several network models and on a real system, which is formed by journal
articles and their respective citations. Surprisingly, the results revealed
that, according to the adopted walking models, the efficiency of self-knowledge
acquisition has only a weak dependency on the topology, search strategy and
localization of the network brain
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