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

SMTBDD: New Form of BDD for Logic Synthesis

The main purpose of the paper is to suggest a new form of BDD – SMTBDD diagram, methods of obtaining, and its basic features. The idea of using SMTBDD diagram in the process of logic synthesis dedicated to FPGA structures is presented. The creation of SMTBDD diagrams is the result of cutting BDD diagram which is the effect of multiple decomposition. The essence of a proposed decomposition method rests on the way of determining the number of necessary ‘g’ bounded functions on the basis of the content of a root table connected with an appropriate SMTBDD diagram. The article presents the methods of searching non-disjoint decomposition using SMTBDD diagrams. Besides, it analyzes the techniques of choosing cutting levels as far as effective technology mapping is concerned. The paper also discusses the results of the experiments which confirm the efficiency of the analyzed decomposition methods

Combinatorial methods for the evaluation of yield and operational reliability of fault-tolerant systems-on-chip

In this paper we develop combinatorial methods for the evaluation of yield and operational reliability of fault-tolerant systems-on-chip. The method for yield computation assumes that defects are produced according to a model in which defects are lethal and affect given components of the system following a distribution common to all defects; the method for the computation of operational reliability also assumes that the fault-tree function of the system is increasing. The distribution of the number of defects is arbitrary. The methods are based on the formulation of, respectively, the yield and the operational reliability as the probability that a given boolean function with multiple-valued variables has value 1. That probability is computed by analyzing a ROMDD (reduced ordered multiple-value decision diagram) representation of the function. For efficiency reasons, a coded ROBDD (reduced ordered binary decision diagram) representation of the function is built first and, then, that coded ROBDD is transformed into the ROMDD required by the methods. We present numerical experiments showing that the methods are able to cope with quite large systems in moderate CPU times.Postprint (published version

Linking Domain Models and Process Models for Reference Model Configuration

. Linking domain models and process models for reference model configuration. In Proceedings of the 10th International Workshop on Reference Modeling (RefMod 2007), 24 September 2007, Brisbane, Australia (pp. 13-24). Brisbane, Australia: QUT. Document status and date: Published: 01/01/2007 Document Version: Accepted manuscript including changes made at the peer-review stage Please check the document version of this publication: • A submitted manuscript is the version of the article upon submission and before peer-review. There can be important differences between the submitted version and the official published version of record. People interested in the research are advised to contact the author for the final version of the publication, or visit the DOI to the publisher's website. • The final author version and the galley proof are versions of the publication after peer review. • The final published version features the final layout of the paper including the volume, issue and page numbers. Link to publication General rights Copyright and moral rights for the publications made accessible in the public portal are retained by the authors and/or other copyright owners and it is a condition of accessing publications that users recognise and abide by the legal requirements associated with these rights. • Users may download and print one copy of any publication from the public portal for the purpose of private study or research. • You may not further distribute the material or use it for any profit-making activity or commercial gain • You may freely distribute the URL identifying the publication in the public portal. If the publication is distributed under the terms of Article 25fa of the Dutch Copyright Act, indicated by the "Taverne" license above, please follow below link for the End User Agreement: Abstract. Reference process models capture common practices in a given domain and variations thereof. Such models are intended to be configured in a specific setting, leading to individualized process models. Although the advantages of reference process models are widely accepted, their configuration still requires a high degree of modeling expertise. Thus users not only need to be domain experts, but also need to master the notation in which the reference process model is captured. In this paper we propose a framework for reference process modeling wherein the domain variability is represented separately from the actual process model. Domain variability is captured as a questionnaire that reflects the decisions that need to be made during configuration and their interrelationships. This questionnaire allows subject matter experts to configure the process model without requiring them to understand the process modeling notation. The approach guarantees that the resulting process models are correct according to certain constraints. To demonstrate the applicability of the proposal, we have implemented a questionnaire toolset that guides users through the configuration of reference process models captured in two different notations

Logic Design Error Diagnosis and Corrections

Coordinated Science Laboratory was formerly known as Control Systems LaboratoryJoint Services Electronics Program / N00014-90-J-127

On efficient ordered binary decision diagram minimization heuristics based on two-level logic.

by Chun Gu.Thesis (M.Phil.)--Chinese University of Hong Kong, 1999.Includes bibliographical references (leaves 69-71).Abstract also in Chinese.Chapter 1 --- Introduction --- p.3Chapter 2 --- Definitions --- p.7Chapter 3 --- Some Previous Work on OBDD --- p.13Chapter 3.1 --- The Work of Bryant --- p.13Chapter 3.2 --- Some Variations of the OBDD --- p.14Chapter 3.3 --- Previous Work on Variable Ordering of OBDD --- p.16Chapter 3.3.1 --- The FIH Heuristic --- p.16Chapter 3.3.2 --- The Dynamic Variable Ordering --- p.17Chapter 3.3.3 --- The Interleaving method --- p.19Chapter 4 --- Two Level Logic Function and OBDD --- p.21Chapter 5 --- DSCF Algorithm --- p.25Chapter 6 --- Thin Boolean Function --- p.33Chapter 6.1 --- The Structure and Properties of thin Boolean functions --- p.33Chapter 6.1.1 --- The construction of Thin OBDDs --- p.33Chapter 6.1.2 --- Properties of Thin Boolean Functions --- p.38Chapter 6.1.3 --- Thin Factored Functions --- p.49Chapter 6.2 --- The Revised DSCF Algorithm --- p.52Chapter 6.3 --- Experimental Results --- p.54Chapter 7 --- A Pattern Merging Algorithm --- p.59Chapter 7.1 --- Merging of Patterns --- p.60Chapter 7.2 --- The Algorithm --- p.62Chapter 7.3 --- Experiments and Conclusion --- p.65Chapter 8 --- Conclusions --- p.6