550 research outputs found
Community detection and role identification in directed networks: understanding the Twitter network of the care.data debate
With the rise of social media as an important channel for the debate and discussion of public affairs, online social networks such as Twitter have become important platforms for public information and engagement by policy makers. To communicate effectively through Twitter, policy makers need to understand how influence and interest propagate within its network of users. In this chapter we use graph-theoretic methods to analyse the Twitter debate surrounding NHS Englands controversial care.data scheme. Directionality is a crucial feature of the Twitter social graph - information flows from the followed to the followers - but is often ignored in social network analyses; our methods are based on the behaviour of dynamic processes on the network and can be applied naturally to directed networks. We uncover robust communities of users and show that these communities reflect how information flows through the Twitter network. We are also able to classify users by their differing roles in directing the flow of information through the network. Our methods and results will be useful to policy makers who would like to use Twitter effectively as a communication medium
The exit time finite state projection scheme: bounding exit distributions and occupation measures of continuous-time Markov chains
We introduce the exit time finite state projection (ETFSP) scheme, a truncation- based method that yields approximations to the exit distribution and occupation measure associated with the time of exit from a domain (i.e., the time of first passage to the complement of the domain) of time-homogeneous continuous-time Markov chains. We prove that: (i) the computed approximations bound the measures from below; (ii) the total variation distances between the approximations and the measures decrease monotonically as states are added to the truncation; and (iii) the scheme converges, in the sense that, as the truncation tends to the entire state space, the total variation distances tend to zero. Furthermore, we give a computable bound on the total variation distance between the exit distribution and its approximation, and we delineate the cases in which the bound is sharp. We also revisit the related finite state projection scheme and give a comprehensive account of its theoretical properties. We demonstrate the use of the ETFSP scheme by applying it to two biological examples: the computation of the first passage time associated with the expression of a gene, and the fixation times of competing species subject to demographic noise
Uncovering allosteric pathways in caspase-1 with Markov transient analysis and multiscale community detection
Allosteric regulation at distant sites is central to many cellular processes.
In particular, allosteric sites in proteins are a major target to increase the
range and selectivity of new drugs, and there is a need for methods capable of
identifying intra-molecular signalling pathways leading to allosteric effects.
Here, we use an atomistic graph-theoretical approach that exploits Markov
transients to extract such pathways and exemplify our results in an important
allosteric protein, caspase-1. Firstly, we use Markov Stability community
detection to perform a multiscale analysis of the structure of caspase-1 which
reveals that the active conformation has a weaker, less compartmentalised
large-scale structure as compared to the inactive conformation, resulting in
greater intra-protein coherence and signal propagation. We also carry out a
full computational point mutagenesis and identify that only a few residues are
critical to such structural coherence. Secondly, we characterise explicitly the
transients of random walks originating at the active site and predict the
location of a known allosteric site in this protein quantifying the
contribution of individual bonds to the communication pathway between the
active and allosteric sites. Several of the bonds we find have been shown
experimentally to be functionally critical, but we also predict a number of as
yet unidentified bonds which may contribute to the pathway. Our approach offers
a computationally inexpensive method for the identification of allosteric sites
and communication pathways in proteins using a fully atomistic description.Comment: 14 pages, 8 figure
Low-Temperature Excitations of Dilute Lattice Spin Glasses
A new approach to exploring low-temperature excitations in finite-dimensional
lattice spin glasses is proposed. By focusing on bond-diluted lattices just
above the percolation threshold, large system sizes can be obtained which
lead to enhanced scaling regimes and more accurate exponents. Furthermore, this
method in principle remains practical for any dimension, yielding exponents
that so far have been elusive. This approach is demonstrated by determining the
stiffness exponent for dimensions , (the upper critical dimension),
and . Key is the application of an exact reduction algorithm, which
eliminates a large fraction of spins, so that the reduced lattices never exceed
variables for sizes as large as L=30 in , L=9 in , or L=8
in . Finite size scaling analysis gives for ,
significantly improving on previous work. The results for and ,
and , are entirely new and are compared with
mean-field predictions made for d>=6.Comment: 7 pages, LaTex, 7 ps-figures included, added result for stiffness in
d=7, as to appear in Europhysics Letters (see
http://www.physics.emory.edu/faculty/boettcher/ for related information
Synchronization in small-world systems
We quantify the dynamical implications of the small-world phenomenon. We
consider the generic synchronization of oscillator networks of arbitrary
topology, and link the linear stability of the synchronous state to an
algebraic condition of the Laplacian of the graph. We show numerically that the
addition of random shortcuts produces improved network synchronizability.
Further, we use a perturbation analysis to place the synchronization threshold
in relation to the boundaries of the small-world region. Our results also show
that small-worlds synchronize as efficiently as random graphs and hypercubes,
and more so than standard constructive graphs
Methodology of Building An Expert System Using Induction Rules with Structured Programming
This article aims to give an idea of how to develop expert systems to help society in all senses. The rules of induction used with MYCIN can help us in medicine, agricultura, and the financial system, implementing an inference engine with different rules so that in case there are no people, the expert system can diagnose in a clear and precise way, based on a knowledge base with some consequents and their corresponding antecedents, these artificial intelligence algorithms used will allow us to determine what type of consequence or conclusion it gives us, of the chaining of several antecedents with structured programming to give us its corresponding answer, then this research is aimed at obtaining an agile methodology for expert systems that will help us in the development and implementation of this type of systems.
Keywords: algorithms of artificial intelligence, knowledge base, artificial intelligence, inference machine, production rules, real-time systems, expert system
Kernel-based independence tests for causal structure learning on functional data
Measurements of systems taken along a continuous functional dimension, such as time or space, are ubiquitous in many fields, from the physical and biological sciences to economics and engineering. Such measurements can be viewed as realisations of an underlying smooth process sampled over the continuum. However, traditional methods for independence testing and causal learning are not directly applicable to such data, as they do not take into account the dependence along the functional dimension. By using specifically designed kernels, we introduce statistical tests for bivariate, joint, and conditional independence for functional variables. Our method not only extends the applicability to functional data of the Hilbert–Schmidt independence criterion (hsic) and its d-variate version (d-hsic), but also allows us to introduce a test for conditional independence by defining a novel statistic for the conditional permutation test (cpt) based on the Hilbert–Schmidt conditional independence criterion (hscic), with optimised regularisation strength estimated through an evaluation rejection rate. Our empirical results of the size and power of these tests on synthetic functional data show good performance, and we then exemplify their application to several constraint- and regression-based causal structure learning problems, including both synthetic examples and real socioeconomic data
Physical consequences of PNP and the DMRG-annealing conjecture
Computational complexity theory contains a corpus of theorems and conjectures
regarding the time a Turing machine will need to solve certain types of
problems as a function of the input size. Nature {\em need not} be a Turing
machine and, thus, these theorems do not apply directly to it. But {\em
classical simulations} of physical processes are programs running on Turing
machines and, as such, are subject to them. In this work, computational
complexity theory is applied to classical simulations of systems performing an
adiabatic quantum computation (AQC), based on an annealed extension of the
density matrix renormalization group (DMRG). We conjecture that the
computational time required for those classical simulations is controlled
solely by the {\em maximal entanglement} found during the process. Thus, lower
bounds on the growth of entanglement with the system size can be provided. In
some cases, quantum phase transitions can be predicted to take place in certain
inhomogeneous systems. Concretely, physical conclusions are drawn from the
assumption that the complexity classes {\bf P} and {\bf NP} differ. As a
by-product, an alternative measure of entanglement is proposed which, via
Chebyshev's inequality, allows to establish strict bounds on the required
computational time.Comment: Accepted for publication in JSTA
- …