255,025 research outputs found
Exploring the Evolution of Node Neighborhoods in Dynamic Networks
Dynamic Networks are a popular way of modeling and studying the behavior of
evolving systems. However, their analysis constitutes a relatively recent
subfield of Network Science, and the number of available tools is consequently
much smaller than for static networks. In this work, we propose a method
specifically designed to take advantage of the longitudinal nature of dynamic
networks. It characterizes each individual node by studying the evolution of
its direct neighborhood, based on the assumption that the way this neighborhood
changes reflects the role and position of the node in the whole network. For
this purpose, we define the concept of \textit{neighborhood event}, which
corresponds to the various transformations such groups of nodes can undergo,
and describe an algorithm for detecting such events. We demonstrate the
interest of our method on three real-world networks: DBLP, LastFM and Enron. We
apply frequent pattern mining to extract meaningful information from temporal
sequences of neighborhood events. This results in the identification of
behavioral trends emerging in the whole network, as well as the individual
characterization of specific nodes. We also perform a cluster analysis, which
reveals that, in all three networks, one can distinguish two types of nodes
exhibiting different behaviors: a very small group of active nodes, whose
neighborhood undergo diverse and frequent events, and a very large group of
stable nodes
To Transformers and Beyond: Large Language Models for the Genome
In the rapidly evolving landscape of genomics, deep learning has emerged as a
useful tool for tackling complex computational challenges. This review focuses
on the transformative role of Large Language Models (LLMs), which are mostly
based on the transformer architecture, in genomics. Building on the foundation
of traditional convolutional neural networks and recurrent neural networks, we
explore both the strengths and limitations of transformers and other LLMs for
genomics. Additionally, we contemplate the future of genomic modeling beyond
the transformer architecture based on current trends in research. The paper
aims to serve as a guide for computational biologists and computer scientists
interested in LLMs for genomic data. We hope the paper can also serve as an
educational introduction and discussion for biologists to a fundamental shift
in how we will be analyzing genomic data in the future
Rethinking Suburban Governance in the CEE Region: A Comparison of Two Municipalities in Poland and Lithuania
In this article, we seek to analyse and compare the modalities of suburban governance in Polish and Lithuanian municipalities looking at the territorial development trends typical for the Central Eastern Europe region. The theoretical elaborations on suburban governance are evolving towards the analysis of constellations of diverse actors, institutions and processes that define the politics and design of suburban spaces. We assume that there are similarities and differences in suburban governance in the analysed localities compared to Western countries in terms of networks, actors and territorialisation of local politics. Despite both suburban municipalities showing similarities in suburban development patterns (growing middle-class population, economic capital accumulation, suburban sprawl and interconnectedness with the metropolitan zone), the analysis reveals the main differences in terms of composition and importance of horizontal and vertical networks, the role of local stakeholders and collective action. The article concludes that both localities represent a specific approach to suburban governance marked by low stakeholders’ participation, dependence on the top down vertical state and regional networks and the creation of urban-suburban policies within metropolitan areas
Understanding the Relationship between Cybercrime and Human Behavior through Criminological Theories and Social Networking Sites
This chapter presents an overview of emerging issues in the psychology of human behaviour and the evolving nature of cyber threats. Theories of crime and empirical studies on user victimisation as seen on social networks are reviewed. The chapter reflects on the role of social engineering as the entry point of many sophisticated attacks and highlights the relevance of the human element as the starting point of implementing cyber security programmes in organisations as well as securing individual online behaviour. Specifically, the criminological theories of crime (i.e. self-control and rational choice theories) are discussed. Issues associated with the emerging trends in human behaviour research and ethics are presented for further discussion. The chapter concludes with a set of open research questions warranting immediate academic attention to avoid the exponential growth of future information breaches
Born to learn: The inspiration, progress, and future of evolved plastic artificial neural networks
Biological plastic neural networks are systems of extraordinary computational
capabilities shaped by evolution, development, and lifetime learning. The
interplay of these elements leads to the emergence of adaptive behavior and
intelligence. Inspired by such intricate natural phenomena, Evolved Plastic
Artificial Neural Networks (EPANNs) use simulated evolution in-silico to breed
plastic neural networks with a large variety of dynamics, architectures, and
plasticity rules: these artificial systems are composed of inputs, outputs, and
plastic components that change in response to experiences in an environment.
These systems may autonomously discover novel adaptive algorithms, and lead to
hypotheses on the emergence of biological adaptation. EPANNs have seen
considerable progress over the last two decades. Current scientific and
technological advances in artificial neural networks are now setting the
conditions for radically new approaches and results. In particular, the
limitations of hand-designed networks could be overcome by more flexible and
innovative solutions. This paper brings together a variety of inspiring ideas
that define the field of EPANNs. The main methods and results are reviewed.
Finally, new opportunities and developments are presented
In silico transitions to multicellularity
The emergence of multicellularity and developmental programs are among the
major problems of evolutionary biology. Traditionally, research in this area
has been based on the combination of data analysis and experimental work on one
hand and theoretical approximations on the other. A third possibility is
provided by computer simulation models, which allow to both simulate reality
and explore alternative possibilities. These in silico models offer a powerful
window to the possible and the actual by means of modeling how virtual cells
and groups of cells can evolve complex interactions beyond a set of isolated
entities. Here we present several examples of such models, each one
illustrating the potential for artificial modeling of the transition to
multicellularity.Comment: 21 pages, 10 figures. Book chapter of Evolutionary transitions to
multicellular life (Springer
Co-evolution of Selection and Influence in Social Networks
Many networks are complex dynamical systems, where both attributes of nodes
and topology of the network (link structure) can change with time. We propose a
model of co-evolving networks where both node at- tributes and network
structure evolve under mutual influence. Specifically, we consider a mixed
membership stochastic blockmodel, where the probability of observing a link
between two nodes depends on their current membership vectors, while those
membership vectors themselves evolve in the presence of a link between the
nodes. Thus, the network is shaped by the interaction of stochastic processes
describing the nodes, while the processes themselves are influenced by the
changing network structure. We derive an efficient variational inference
procedure for our model, and validate the model on both synthetic and
real-world data.Comment: In Proc. of the Twenty-Fifth Conference on Artificial Intelligence
(AAAI-11
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