17,660 research outputs found
Growth-Driven Percolations: The Dynamics of Community Formation in Neuronal Systems
The quintessential property of neuronal systems is their intensive patterns
of selective synaptic connections. The current work describes a physics-based
approach to neuronal shape modeling and synthesis and its consideration for the
simulation of neuronal development and the formation of neuronal communities.
Starting from images of real neurons, geometrical measurements are obtained and
used to construct probabilistic models which can be subsequently sampled in
order to produce morphologically realistic neuronal cells. Such cells are
progressively grown while monitoring their connections along time, which are
analysed in terms of percolation concepts. However, unlike traditional
percolation, the critical point is verified along the growth stages, not the
density of cells, which remains constant throughout the neuronal growth
dynamics. It is shown, through simulations, that growing beta cells tend to
reach percolation sooner than the alpha counterparts with the same diameter.
Also, the percolation becomes more abrupt for higher densities of cells, being
markedly sharper for the beta cells.Comment: 8 pages, 10 figure
Self-Healing Protocols for Connectivity Maintenance in Unstructured Overlays
In this paper, we discuss on the use of self-organizing protocols to improve
the reliability of dynamic Peer-to-Peer (P2P) overlay networks. Two similar
approaches are studied, which are based on local knowledge of the nodes' 2nd
neighborhood. The first scheme is a simple protocol requiring interactions
among nodes and their direct neighbors. The second scheme adds a check on the
Edge Clustering Coefficient (ECC), a local measure that allows determining
edges connecting different clusters in the network. The performed simulation
assessment evaluates these protocols over uniform networks, clustered networks
and scale-free networks. Different failure modes are considered. Results
demonstrate the effectiveness of the proposal.Comment: The paper has been accepted to the journal Peer-to-Peer Networking
and Applications. The final publication is available at Springer via
http://dx.doi.org/10.1007/s12083-015-0384-
GoFFish: A Sub-Graph Centric Framework for Large-Scale Graph Analytics
Large scale graph processing is a major research area for Big Data
exploration. Vertex centric programming models like Pregel are gaining traction
due to their simple abstraction that allows for scalable execution on
distributed systems naturally. However, there are limitations to this approach
which cause vertex centric algorithms to under-perform due to poor compute to
communication overhead ratio and slow convergence of iterative superstep. In
this paper we introduce GoFFish a scalable sub-graph centric framework
co-designed with a distributed persistent graph storage for large scale graph
analytics on commodity clusters. We introduce a sub-graph centric programming
abstraction that combines the scalability of a vertex centric approach with the
flexibility of shared memory sub-graph computation. We map Connected
Components, SSSP and PageRank algorithms to this model to illustrate its
flexibility. Further, we empirically analyze GoFFish using several real world
graphs and demonstrate its significant performance improvement, orders of
magnitude in some cases, compared to Apache Giraph, the leading open source
vertex centric implementation.Comment: Under review by a conference, 201
Organic Design of Massively Distributed Systems: A Complex Networks Perspective
The vision of Organic Computing addresses challenges that arise in the design
of future information systems that are comprised of numerous, heterogeneous,
resource-constrained and error-prone components or devices. Here, the notion
organic particularly highlights the idea that, in order to be manageable, such
systems should exhibit self-organization, self-adaptation and self-healing
characteristics similar to those of biological systems. In recent years, the
principles underlying many of the interesting characteristics of natural
systems have been investigated from the perspective of complex systems science,
particularly using the conceptual framework of statistical physics and
statistical mechanics. In this article, we review some of the interesting
relations between statistical physics and networked systems and discuss
applications in the engineering of organic networked computing systems with
predictable, quantifiable and controllable self-* properties.Comment: 17 pages, 14 figures, preprint of submission to Informatik-Spektrum
published by Springe
Temporal Networks
A great variety of systems in nature, society and technology -- from the web
of sexual contacts to the Internet, from the nervous system to power grids --
can be modeled as graphs of vertices coupled by edges. The network structure,
describing how the graph is wired, helps us understand, predict and optimize
the behavior of dynamical systems. In many cases, however, the edges are not
continuously active. As an example, in networks of communication via email,
text messages, or phone calls, edges represent sequences of instantaneous or
practically instantaneous contacts. In some cases, edges are active for
non-negligible periods of time: e.g., the proximity patterns of inpatients at
hospitals can be represented by a graph where an edge between two individuals
is on throughout the time they are at the same ward. Like network topology, the
temporal structure of edge activations can affect dynamics of systems
interacting through the network, from disease contagion on the network of
patients to information diffusion over an e-mail network. In this review, we
present the emergent field of temporal networks, and discuss methods for
analyzing topological and temporal structure and models for elucidating their
relation to the behavior of dynamical systems. In the light of traditional
network theory, one can see this framework as moving the information of when
things happen from the dynamical system on the network, to the network itself.
Since fundamental properties, such as the transitivity of edges, do not
necessarily hold in temporal networks, many of these methods need to be quite
different from those for static networks
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Communicability across evolving networks
Many natural and technological applications generate time ordered sequences of networks, deïŹned over a ïŹxed set of nodes; for example time-stamped information about âwho phoned whoâ or âwho came into contact with whoâ arise naturally in studies of communication and the spread of disease. Concepts and algorithms for static networks do not immediately carry through to this dynamic setting. For example, suppose A and B interact in the morning, and then B and C interact in the afternoon. Information, or disease, may then pass from A to C, but not vice versa. This subtlety is lost if we simply summarize using the daily aggregate network given by the chain A-B-C. However, using a natural deïŹnition of a walk on an evolving network, we show that classic centrality measures from the static setting can be extended in a computationally convenient manner. In particular, communicability indices can be computed to summarize the ability of each node to broadcast and receive information. The computations involve basic operations in linear algebra, and the asymmetry caused by timeâs arrow is captured naturally through the non-mutativity of matrix-matrix multiplication. Illustrative examples are given for both synthetic and real-world communication data sets. We also discuss the use of the new centrality measures for real-time monitoring and prediction
Fast Search for Dynamic Multi-Relational Graphs
Acting on time-critical events by processing ever growing social media or
news streams is a major technical challenge. Many of these data sources can be
modeled as multi-relational graphs. Continuous queries or techniques to search
for rare events that typically arise in monitoring applications have been
studied extensively for relational databases. This work is dedicated to answer
the question that emerges naturally: how can we efficiently execute a
continuous query on a dynamic graph? This paper presents an exact subgraph
search algorithm that exploits the temporal characteristics of representative
queries for online news or social media monitoring. The algorithm is based on a
novel data structure called the Subgraph Join Tree (SJ-Tree) that leverages the
structural and semantic characteristics of the underlying multi-relational
graph. The paper concludes with extensive experimentation on several real-world
datasets that demonstrates the validity of this approach.Comment: SIGMOD Workshop on Dynamic Networks Management and Mining (DyNetMM),
201
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