17,977 research outputs found
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
Homogeneous and Scalable Gene Expression Regulatory Networks with Random Layouts of Switching Parameters
We consider a model of large regulatory gene expression networks where the
thresholds activating the sigmoidal interactions between genes and the signs of
these interactions are shuffled randomly. Such an approach allows for a
qualitative understanding of network dynamics in a lack of empirical data
concerning the large genomes of living organisms. Local dynamics of network
nodes exhibits the multistationarity and oscillations and depends crucially
upon the global topology of a "maximal" graph (comprising of all possible
interactions between genes in the network). The long time behavior observed in
the network defined on the homogeneous "maximal" graphs is featured by the
fraction of positive interactions () allowed between genes.
There exists a critical value such that if , the
oscillations persist in the system, otherwise, when it tends to
a fixed point (which position in the phase space is determined by the initial
conditions and the certain layout of switching parameters). In networks defined
on the inhomogeneous directed graphs depleted in cycles, no oscillations arise
in the system even if the negative interactions in between genes present
therein in abundance (). For such networks, the bidirectional edges
(if occur) influence on the dynamics essentially. In particular, if a number of
edges in the "maximal" graph is bidirectional, oscillations can arise and
persist in the system at any low rate of negative interactions between genes
(). Local dynamics observed in the inhomogeneous scalable regulatory
networks is less sensitive to the choice of initial conditions. The scale free
networks demonstrate their high error tolerance.Comment: LaTeX, 30 pages, 20 picture
A Distributed Multilevel Force-directed Algorithm
The wide availability of powerful and inexpensive cloud computing services
naturally motivates the study of distributed graph layout algorithms, able to
scale to very large graphs. Nowadays, to process Big Data, companies are
increasingly relying on PaaS infrastructures rather than buying and maintaining
complex and expensive hardware. So far, only a few examples of basic
force-directed algorithms that work in a distributed environment have been
described. Instead, the design of a distributed multilevel force-directed
algorithm is a much more challenging task, not yet addressed. We present the
first multilevel force-directed algorithm based on a distributed vertex-centric
paradigm, and its implementation on Giraph, a popular platform for distributed
graph algorithms. Experiments show the effectiveness and the scalability of the
approach. Using an inexpensive cloud computing service of Amazon, we draw
graphs with ten million edges in about 60 minutes.Comment: Appears in the Proceedings of the 24th International Symposium on
Graph Drawing and Network Visualization (GD 2016
GraphCombEx: A Software Tool for Exploration of Combinatorial Optimisation Properties of Large Graphs
We present a prototype of a software tool for exploration of multiple
combinatorial optimisation problems in large real-world and synthetic complex
networks. Our tool, called GraphCombEx (an acronym of Graph Combinatorial
Explorer), provides a unified framework for scalable computation and
presentation of high-quality suboptimal solutions and bounds for a number of
widely studied combinatorial optimisation problems. Efficient representation
and applicability to large-scale graphs and complex networks are particularly
considered in its design. The problems currently supported include maximum
clique, graph colouring, maximum independent set, minimum vertex clique
covering, minimum dominating set, as well as the longest simple cycle problem.
Suboptimal solutions and intervals for optimal objective values are estimated
using scalable heuristics. The tool is designed with extensibility in mind,
with the view of further problems and both new fast and high-performance
heuristics to be added in the future. GraphCombEx has already been successfully
used as a support tool in a number of recent research studies using
combinatorial optimisation to analyse complex networks, indicating its promise
as a research software tool
TGVizTab: An ontology visualisation extension for Protégé
Ontologies are gaining a lot of interest and many are being developed to provide a variety of knowledge services. There is an increasing need for tools to graphically and in-teractively visualise such modelling structures to enhance their clarification, verification and analysis. Protégé 2000 is one of the most popular ontology modelling tools currently available. This paper introduces TGVizTab; a new Protégé plugin based on TouchGraph technology to graphically visualise Protégé?s ontologies
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