24,708 research outputs found
Development of a Parallel BAT and Its Applications in Binary-state Network Reliability Problems
Various networks are broadly and deeply applied in real-life applications.
Reliability is the most important index for measuring the performance of all
network types. Among the various algorithms, only implicit enumeration
algorithms, such as depth-first-search, breadth-search-first, universal
generating function methodology, binary-decision diagram, and
binary-addition-tree algorithm (BAT), can be used to calculate the exact
network reliability. However, implicit enumeration algorithms can only be used
to solve small-scale network reliability problems. The BAT was recently
proposed as a simple, fast, easy-to-code, and flexible make-to-fit
exact-solution algorithm. Based on the experimental results, the BAT and its
variants outperformed other implicit enumeration algorithms. Hence, to overcome
the above-mentioned obstacle as a result of the size problem, a new parallel
BAT (PBAT) was proposed to improve the BAT based on compute multithread
architecture to calculate the binary-state network reliability problem, which
is fundamental for all types of network reliability problems. From the analysis
of the time complexity and experiments conducted on 20 benchmarks of
binary-state network reliability problems, PBAT was able to efficiently solve
medium-scale network reliability problems
Certainty Closure: Reliable Constraint Reasoning with Incomplete or Erroneous Data
Constraint Programming (CP) has proved an effective paradigm to model and
solve difficult combinatorial satisfaction and optimisation problems from
disparate domains. Many such problems arising from the commercial world are
permeated by data uncertainty. Existing CP approaches that accommodate
uncertainty are less suited to uncertainty arising due to incomplete and
erroneous data, because they do not build reliable models and solutions
guaranteed to address the user's genuine problem as she perceives it. Other
fields such as reliable computation offer combinations of models and associated
methods to handle these types of uncertain data, but lack an expressive
framework characterising the resolution methodology independently of the model.
We present a unifying framework that extends the CP formalism in both model
and solutions, to tackle ill-defined combinatorial problems with incomplete or
erroneous data. The certainty closure framework brings together modelling and
solving methodologies from different fields into the CP paradigm to provide
reliable and efficient approches for uncertain constraint problems. We
demonstrate the applicability of the framework on a case study in network
diagnosis. We define resolution forms that give generic templates, and their
associated operational semantics, to derive practical solution methods for
reliable solutions.Comment: Revised versio
Exact Computation of Influence Spread by Binary Decision Diagrams
Evaluating influence spread in social networks is a fundamental procedure to
estimate the word-of-mouth effect in viral marketing. There are enormous
studies about this topic; however, under the standard stochastic cascade
models, the exact computation of influence spread is known to be #P-hard. Thus,
the existing studies have used Monte-Carlo simulation-based approximations to
avoid exact computation.
We propose the first algorithm to compute influence spread exactly under the
independent cascade model. The algorithm first constructs binary decision
diagrams (BDDs) for all possible realizations of influence spread, then
computes influence spread by dynamic programming on the constructed BDDs. To
construct the BDDs efficiently, we designed a new frontier-based search-type
procedure. The constructed BDDs can also be used to solve other
influence-spread related problems, such as random sampling without rejection,
conditional influence spread evaluation, dynamic probability update, and
gradient computation for probability optimization problems.
We conducted computational experiments to evaluate the proposed algorithm.
The algorithm successfully computed influence spread on real-world networks
with a hundred edges in a reasonable time, which is quite impossible by the
naive algorithm. We also conducted an experiment to evaluate the accuracy of
the Monte-Carlo simulation-based approximation by comparing exact influence
spread obtained by the proposed algorithm.Comment: WWW'1
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