13 research outputs found
Physical Models in Community Detection with Applications to Identifying Structure in Complex Amorphous Systems
We present an exceptionally accurate spin-glass-type Potts model for the graph theoretic problem of community detection. With a simple algorithm, we find that our approach is exceptionally accurate, robust to the effects of noise, and competitive with the best currently available algorithms in terms of speed and the size of solvable systems. Being a local measure of community structure, our Potts model is free from a resolution limit that hinders community solutions for some popular community detection models. It further remains a local measure on weighted and directed graphs. We apply our community detection method to accurately and quantitatively evaluate the multi-scale: multiresolution ) structure of a graph. Our multiresolution algorithm calculates correlations among multiple copies: replicas ) of the same graph over a range of resolutions. Significant multiresolution structures are identified by strongly correlated replicas. The average normalized mutual information and variation of information give a quantitative estimate of the best resolutions and indicate the relative strength of the structures in the graph. We further investigate a phase transition effect in community detection, and we elaborate on its relation to analogous physical phase transitions. Finally, we apply our community detection methods to ascertain the most natural complex amorphous structures in two model glasses in an unbiased manner. We construct a model graph for the physical systems using the potential energy to generate weighted edge relationships for all pairs of atoms. We then solve for the communities within the model network and associate the best communities with the natural structures in the physical systems
An interacting replica approach applied to the traveling salesman problem
We present a physics inspired heuristic method for solving combinatorial
optimization problems. Our approach is specifically motivated by the desire to
avoid trapping in metastable local minima- a common occurrence in hard problems
with multiple extrema. Our method involves (i) coupling otherwise independent
simulations of a system ("replicas") via geometrical distances as well as (ii)
probabilistic inference applied to the solutions found by individual replicas.
The {\it ensemble} of replicas evolves as to maximize the inter-replica
correlation while simultaneously minimize the local intra-replica cost function
(e.g., the total path length in the Traveling Salesman Problem within each
replica). We demonstrate how our method improves the performance of rudimentary
local optimization schemes long applied to the NP hard Traveling Salesman
Problem. In particular, we apply our method to the well-known "-opt"
algorithm and examine two particular cases- and . With the aid of
geometrical coupling alone, we are able to determine for the optimum tour
length on systems up to cities (an order of magnitude larger than the
largest systems typically solved by the bare opt). The probabilistic
replica-based inference approach improves even further and determines
the optimal solution of a problem with cities and find tours whose total
length is close to that of the optimal solutions for other systems with a
larger number of cities.Comment: To appear in SAI 2016 conference proceedings 12 pages,17 figure
Multiresolution community detection for megascale networks by information-based replica correlations
We use a Potts model community detection algorithm to accurately and
quantitatively evaluate the hierarchical or multiresolution structure of a
graph. Our multiresolution algorithm calculates correlations among multiple
copies ("replicas") of the same graph over a range of resolutions. Significant
multiresolution structures are identified by strongly correlated replicas. The
average normalized mutual information, the variation of information, and other
measures in principle give a quantitative estimate of the "best" resolutions
and indicate the relative strength of the structures in the graph. Because the
method is based on information comparisons, it can in principle be used with
any community detection model that can examine multiple resolutions. Our
approach may be extended to other optimization problems. As a local measure,
our Potts model avoids the "resolution limit" that affects other popular
models. With this model, our community detection algorithm has an accuracy that
ranks among the best of currently available methods. Using it, we can examine
graphs over 40 million nodes and more than one billion edges. We further report
that the multiresolution variant of our algorithm can solve systems of at least
200000 nodes and 10 million edges on a single processor with exceptionally high
accuracy. For typical cases, we find a super-linear scaling, O(L^{1.3}) for
community detection and O(L^{1.3} log N) for the multiresolution algorithm
where L is the number of edges and N is the number of nodes in the system.Comment: 19 pages, 14 figures, published version with minor change
Local resolution-limit-free Potts model for community detection
We report on an exceptionally accurate spin-glass-type Potts model for
community detection. With a simple algorithm, we find that our approach is at
least as accurate as the best currently available algorithms and robust to the
effects of noise. It is also competitive with the best currently available
algorithms in terms of speed and size of solvable systems. We find that the
computational demand often exhibits superlinear scaling L^1.3 where L is the
number of edges in the system, and we have applied the algorithm to synthetic
systems as large as 40x10^6 nodes and over 1x10^9 edges. A previous stumbling
block encountered by popular community detection methods is the so-called
"resolution limit." Being a "local" measure of community structure, our Potts
model is free from this resolution-limit effect, and it further remains a local
measure on weighted and directed graphs. We also address the mitigation of
resolution-limit effects for two other popular Potts models.Comment: 16 pages, 12 figures; title change for Physical Review E, minor
editing, updated reference
Local multiresolution order in community detection
Community detection algorithms attempt to find the best clusters of nodes in
an arbitrary complex network. Multi-scale ("multiresolution") community
detection extends the problem to identify the best network scale(s) for these
clusters. The latter task is generally accomplished by analyzing community
stability simultaneously for all clusters in the network. In the current work,
we extend this general approach to define local multiresolution methods, which
enable the extraction of well-defined local communities even if the global
community structure is vaguely defined in an average sense. Toward this end, we
propose measures analogous to variation of information and normalized mutual
information that are used to quantitatively identify the best resolution(s) at
the community level based on correlations between clusters in
independently-solved systems. We demonstrate our method on two constructed
networks as well as a real network and draw inferences about local community
strength. Our approach is independent of the applied community detection
algorithm save for the inherent requirement that the method be able to identify
communities across different network scales, with appropriate changes to
account for how different resolutions are evaluated or defined in a particular
community detection method. It should, in principle, easily adapt to
alternative community comparison measures.Comment: 19 pages, 11 figure
A Replica Inference Approach to Unsupervised Multi-Scale Image Segmentation
We apply a replica inference based Potts model method to unsupervised image
segmentation on multiple scales. This approach was inspired by the statistical
mechanics problem of "community detection" and its phase diagram. Specifically,
the problem is cast as identifying tightly bound clusters ("communities" or
"solutes") against a background or "solvent". Within our multiresolution
approach, we compute information theory based correlations among multiple
solutions ("replicas") of the same graph over a range of resolutions.
Significant multiresolution structures are identified by replica correlations
as manifest in information theory overlaps. With the aid of these correlations
as well as thermodynamic measures, the phase diagram of the corresponding Potts
model is analyzed both at zero and finite temperatures. Optimal parameters
corresponding to a sensible unsupervised segmentation correspond to the "easy
phase" of the Potts model. Our algorithm is fast and shown to be at least as
accurate as the best algorithms to date and to be especially suited to the
detection of camouflaged images.Comment: 26 pages, 22 figure
Center of Mass Correction to an Error-Prone Undergraduate Centripetal Force Experiment
In this undergraduate laboratory experiment we measure the centripetal force acting on a bob in uniform circular motion. As the experiment was originally designed, it consistently yielded large errors due to a subtle twist of the bob as the mass was increased incrementally. This error is due to the fact that the center of mass changes relative position as the mass is incremented; therefore, the spring that provides the centripetal force for the apparatus causes an unintended torque on the bob. A solution to the problem consists of positioning the incremental masses so that the center of mass does not change position. This simple correction provides a useful lesson on redesigning an undergraduate laboratory experiment to obtain better agreement with theory. (C) 2003 American Association of Physics Teachers