436,184 research outputs found
Parallel Toolkit for Measuring the Quality of Network Community Structure
Many networks display community structure which identifies groups of nodes
within which connections are denser than between them. Detecting and
characterizing such community structure, which is known as community detection,
is one of the fundamental issues in the study of network systems. It has
received a considerable attention in the last years. Numerous techniques have
been developed for both efficient and effective community detection. Among
them, the most efficient algorithm is the label propagation algorithm whose
computational complexity is O(|E|). Although it is linear in the number of
edges, the running time is still too long for very large networks, creating the
need for parallel community detection. Also, computing community quality
metrics for community structure is computationally expensive both with and
without ground truth. However, to date we are not aware of any effort to
introduce parallelism for this problem. In this paper, we provide a parallel
toolkit to calculate the values of such metrics. We evaluate the parallel
algorithms on both distributed memory machine and shared memory machine. The
experimental results show that they yield a significant performance gain over
sequential execution in terms of total running time, speedup, and efficiency.Comment: 8 pages; in Network Intelligence Conference (ENIC), 2014 Europea
Optimal and fast detection of spatial clusters with scan statistics
We consider the detection of multivariate spatial clusters in the Bernoulli
model with locations, where the design distribution has weakly dependent
marginals. The locations are scanned with a rectangular window with sides
parallel to the axes and with varying sizes and aspect ratios. Multivariate
scan statistics pose a statistical problem due to the multiple testing over
many scan windows, as well as a computational problem because statistics have
to be evaluated on many windows. This paper introduces methodology that leads
to both statistically optimal inference and computationally efficient
algorithms. The main difference to the traditional calibration of scan
statistics is the concept of grouping scan windows according to their sizes,
and then applying different critical values to different groups. It is shown
that this calibration of the scan statistic results in optimal inference for
spatial clusters on both small scales and on large scales, as well as in the
case where the cluster lives on one of the marginals. Methodology is introduced
that allows for an efficient approximation of the set of all rectangles while
still guaranteeing the statistical optimality results described above. It is
shown that the resulting scan statistic has a computational complexity that is
almost linear in .Comment: Published in at http://dx.doi.org/10.1214/09-AOS732 the Annals of
Statistics (http://www.imstat.org/aos/) by the Institute of Mathematical
Statistics (http://www.imstat.org
A parallel evolutionary approach to solving systems of equations in polycyclic groups
AbstractThe Anshel–Anshel–Goldfeld (AAG) key exchange protocol is based upon the multiple conjugacy problem for a finitely-presented group. The hardness in breaking this protocol relies on the supposed difficulty in solving the corresponding equations for the conjugating element in the group. Two such protocols based on polycyclic groups as a platform were recently proposed and were shown to be resistant to length-based attack. In this article we propose a parallel evolutionary approach which runs on multicore high-performance architectures. The approach is shown to be more efficient than previous attempts to break these protocols, and also more successful. Comprehensive data of experiments run with a GAP implementation are provided and compared to the results of earlier length-based attacks. These demonstrate that the proposed platform is not as secure as first thought and also show that existing measures of cryptographic complexity are not optimal. A more accurate alternative measure is suggested. Finally, a linear algebra attack for one of the protocols is introduced.</jats:p
Evaluating Matrix Circuits
The circuit evaluation problem (also known as the compressed word problem)
for finitely generated linear groups is studied. The best upper bound for this
problem is , which is shown by a reduction to polynomial
identity testing. Conversely, the compressed word problem for the linear group
is equivalent to polynomial identity testing. In
the paper, it is shown that the compressed word problem for every finitely
generated nilpotent group is in . Within
the larger class of polycyclic groups we find examples where the compressed
word problem is at least as hard as polynomial identity testing for skew
arithmetic circuits
Predicting Non-linear Cellular Automata Quickly by Decomposing Them into Linear Ones
We show that a wide variety of non-linear cellular automata (CAs) can be
decomposed into a quasidirect product of linear ones. These CAs can be
predicted by parallel circuits of depth O(log^2 t) using gates with binary
inputs, or O(log t) depth if ``sum mod p'' gates with an unbounded number of
inputs are allowed. Thus these CAs can be predicted by (idealized) parallel
computers much faster than by explicit simulation, even though they are
non-linear.
This class includes any CA whose rule, when written as an algebra, is a
solvable group. We also show that CAs based on nilpotent groups can be
predicted in depth O(log t) or O(1) by circuits with binary or ``sum mod p''
gates respectively.
We use these techniques to give an efficient algorithm for a CA rule which,
like elementary CA rule 18, has diffusing defects that annihilate in pairs.
This can be used to predict the motion of defects in rule 18 in O(log^2 t)
parallel time
Parallel Algorithm and Dynamic Exponent for Diffusion-limited Aggregation
A parallel algorithm for ``diffusion-limited aggregation'' (DLA) is described
and analyzed from the perspective of computational complexity. The dynamic
exponent z of the algorithm is defined with respect to the probabilistic
parallel random-access machine (PRAM) model of parallel computation according
to , where L is the cluster size, T is the running time, and the
algorithm uses a number of processors polynomial in L\@. It is argued that
z=D-D_2/2, where D is the fractal dimension and D_2 is the second generalized
dimension. Simulations of DLA are carried out to measure D_2 and to test
scaling assumptions employed in the complexity analysis of the parallel
algorithm. It is plausible that the parallel algorithm attains the minimum
possible value of the dynamic exponent in which case z characterizes the
intrinsic history dependence of DLA.Comment: 24 pages Revtex and 2 figures. A major improvement to the algorithm
and smaller dynamic exponent in this versio
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