5,052 research outputs found
The capacity of multilevel threshold functions
Lower and upper bounds for the capacity of multilevel threshold elements are estimated, using two essentially different enumeration techniques. It is demonstrated that the exact number of multilevel threshold functions depends strongly on the relative topology of the input set. The results correct a previously published estimate and indicate that adding threshold levels enhances the capacity more than adding variables
Estimating the resolution limit of the map equation in community detection
A community detection algorithm is considered to have a resolution limit if
the scale of the smallest modules that can be resolved depends on the size of
the analyzed subnetwork. The resolution limit is known to prevent some
community detection algorithms from accurately identifying the modular
structure of a network. In fact, any global objective function for measuring
the quality of a two-level assignment of nodes into modules must have some sort
of resolution limit or an external resolution parameter. However, it is yet
unknown how the resolution limit affects the so-called map equation, which is
known to be an efficient objective function for community detection. We derive
an analytical estimate and conclude that the resolution limit of the map
equation is set by the total number of links between modules instead of the
total number of links in the full network as for modularity. This mechanism
makes the resolution limit much less restrictive for the map equation than for
modularity, and in practice orders of magnitudes smaller. Furthermore, we argue
that the effect of the resolution limit often results from shoehorning
multi-level modular structures into two-level descriptions. As we show, the
hierarchical map equation effectively eliminates the resolution limit for
networks with nested multi-level modular structures.Comment: 12 pages, 7 figure
Large Graph Analysis in the GMine System
Current applications have produced graphs on the order of hundreds of
thousands of nodes and millions of edges. To take advantage of such graphs, one
must be able to find patterns, outliers and communities. These tasks are better
performed in an interactive environment, where human expertise can guide the
process. For large graphs, though, there are some challenges: the excessive
processing requirements are prohibitive, and drawing hundred-thousand nodes
results in cluttered images hard to comprehend. To cope with these problems, we
propose an innovative framework suited for any kind of tree-like graph visual
design. GMine integrates (a) a representation for graphs organized as
hierarchies of partitions - the concepts of SuperGraph and Graph-Tree; and (b)
a graph summarization methodology - CEPS. Our graph representation deals with
the problem of tracing the connection aspects of a graph hierarchy with sub
linear complexity, allowing one to grasp the neighborhood of a single node or
of a group of nodes in a single click. As a proof of concept, the visual
environment of GMine is instantiated as a system in which large graphs can be
investigated globally and locally
Cut Size Statistics of Graph Bisection Heuristics
We investigate the statistical properties of cut sizes generated by heuristic
algorithms which solve approximately the graph bisection problem. On an
ensemble of sparse random graphs, we find empirically that the distribution of
the cut sizes found by ``local'' algorithms becomes peaked as the number of
vertices in the graphs becomes large. Evidence is given that this distribution
tends towards a Gaussian whose mean and variance scales linearly with the
number of vertices of the graphs. Given the distribution of cut sizes
associated with each heuristic, we provide a ranking procedure which takes into
account both the quality of the solutions and the speed of the algorithms. This
procedure is demonstrated for a selection of local graph bisection heuristics.Comment: 17 pages, 5 figures, submitted to SIAM Journal on Optimization also
available at http://ipnweb.in2p3.fr/~martin
FFTPL: An Analytic Placement Algorithm Using Fast Fourier Transform for Density Equalization
We propose a flat nonlinear placement algorithm FFTPL using fast Fourier
transform for density equalization. The placement instance is modeled as an
electrostatic system with the analogy of density cost to the potential energy.
A well-defined Poisson's equation is proposed for gradient and cost
computation. Our placer outperforms state-of-the-art placers with better
solution quality and efficiency
Multilevel compression of random walks on networks reveals hierarchical organization in large integrated systems
To comprehend the hierarchical organization of large integrated systems, we
introduce the hierarchical map equation, which reveals multilevel structures in
networks. In this information-theoretic approach, we exploit the duality
between compression and pattern detection; by compressing a description of a
random walker as a proxy for real flow on a network, we find regularities in
the network that induce this system-wide flow. Finding the shortest multilevel
description of the random walker therefore gives us the best hierarchical
clustering of the network, the optimal number of levels and modular partition
at each level, with respect to the dynamics on the network. With a novel search
algorithm, we extract and illustrate the rich multilevel organization of
several large social and biological networks. For example, from the global air
traffic network we uncover countries and continents, and from the pattern of
scientific communication we reveal more than 100 scientific fields organized in
four major disciplines: life sciences, physical sciences, ecology and earth
sciences, and social sciences. In general, we find shallow hierarchical
structures in globally interconnected systems, such as neural networks, and
rich multilevel organizations in systems with highly separated regions, such as
road networks.Comment: 11 pages, 5 figures. For associated code, see
http://www.tp.umu.se/~rosvall/code.htm
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