1,491 research outputs found
Outward Influence and Cascade Size Estimation in Billion-scale Networks
Estimating cascade size and nodes' influence is a fundamental task in social,
technological, and biological networks. Yet this task is extremely challenging
due to the sheer size and the structural heterogeneity of networks. We
investigate a new influence measure, termed outward influence (OI), defined as
the (expected) number of nodes that a subset of nodes will activate,
excluding the nodes in S. Thus, OI equals, the de facto standard measure,
influence spread of S minus |S|. OI is not only more informative for nodes with
small influence, but also, critical in designing new effective sampling and
statistical estimation methods.
Based on OI, we propose SIEA/SOIEA, novel methods to estimate influence
spread/outward influence at scale and with rigorous theoretical guarantees. The
proposed methods are built on two novel components 1) IICP an important
sampling method for outward influence, and 2) RSA, a robust mean estimation
method that minimize the number of samples through analyzing variance and range
of random variables. Compared to the state-of-the art for influence estimation,
SIEA is times faster in theory and up to several orders of
magnitude faster in practice. For the first time, influence of nodes in the
networks of billions of edges can be estimated with high accuracy within a few
minutes. Our comprehensive experiments on real-world networks also give
evidence against the popular practice of using a fixed number, e.g. 10K or 20K,
of samples to compute the "ground truth" for influence spread.Comment: 16 pages, SIGMETRICS 201
Importance Sketching of Influence Dynamics in Billion-scale Networks
The blooming availability of traces for social, biological, and communication
networks opens up unprecedented opportunities in analyzing diffusion processes
in networks. However, the sheer sizes of the nowadays networks raise serious
challenges in computational efficiency and scalability.
In this paper, we propose a new hyper-graph sketching framework for inflence
dynamics in networks. The central of our sketching framework, called SKIS, is
an efficient importance sampling algorithm that returns only non-singular
reverse cascades in the network. Comparing to previously developed sketches
like RIS and SKIM, our sketch significantly enhances estimation quality while
substantially reducing processing time and memory-footprint. Further, we
present general strategies of using SKIS to enhance existing algorithms for
influence estimation and influence maximization which are motivated by
practical applications like viral marketing. Using SKIS, we design high-quality
influence oracle for seed sets with average estimation error up to 10x times
smaller than those using RIS and 6x times smaller than SKIM. In addition, our
influence maximization using SKIS substantially improves the quality of
solutions for greedy algorithms. It achieves up to 10x times speed-up and 4x
memory reduction for the fastest RIS-based DSSA algorithm, while maintaining
the same theoretical guarantees.Comment: 12 pages, to appear in ICDM 2017 as a regular pape
Dynamics of horizontal-like maps in higher dimension
We study the regularity of the Green currents and of the equilibrium measure
associated to a horizontal-like map in C^k, under a natural assumption on the
dynamical degrees. We estimate the speed of convergence towards the Green
currents, the decay of correlations for the equilibrium measure and the
Lyapounov exponents. We show in particular that the equilibrium measure is
hyperbolic. We also show that the Green currents are the unique invariant
vertical and horizontal positive closed currents. The results apply, in
particular, to Henon-like maps, to regular polynomial automorphisms of C^k and
to their small pertubations.Comment: Dedicated to Professor Gennadi Henkin on the occasion of his 65th
birthday, 37 pages, to appear in Advances in Mat
On "the authentic damping mechanism" of the phonon damping model
Some general features of the phonon damping model are presented. It is
concluded that the fits performed within this model have no physical content
The binding constraint on firms'growth in developing countries
Firms in developing countries face numerous and serious constraints on their growth, ranging from corruption to lack of infrastructure to inability to access finance. Countries lack the resources to remove all the constraints at once and so would be better off removing the most binding one first. This paper uses data from World Bank Enterprise Surveys in 2006-10 to identify the most binding constraints on firm operations in developing countries. While each country faces a different set of constraints, these constraints also vary by firm characteristics, especially firm size. Across all countries, access to finance is among the most binding constraints; other obstacles appear to matter much less. This result is robust for all regions. Smaller firms must rely more on their own funds to invest and would grow significantly faster if they had greater access to external funds. As a result, a low level of financial development skews the firm size distribution by increasing the relative share of small firms. The results suggest that financing constraints play a significant part in explaining the"missing middle"-- the failure of small firms in developing countries to grow into medium-size or large firms.Access to Finance,Environmental Economics&Policies,Microfinance,Debt Markets,Banks&Banking Reform
Unsupervised deep learning-based reconfigurable intelligent surface aided broadcasting communications in industrial IoTs
This paper presents a general system framework which lays the foundation for Reconfigurable Intelligent Surface (RIS)-enhanced broadcast communications in Industrial Internet of Things (IIoTs). In our system model, we consider multiple sensor clusters co-existing in a smart factory where the direct links between these clusters and a central base station (BS) is blocked completely. In this context, an RIS is utilized to reflect signals broadcast from BS toward cluster heads (CHs) which act as a representative of clusters, where BS only has access to the statistical distribution of the channel state information (CSI). An analytical upper bound of the total ergodic spectral efficiency and an approximation of outage probability are derived. Based on these analytical results, two algorithms are introduced to control the phase shifts at RIS, which are the Riemannian conjugate gradient (RCG) method and the deep neural network (DNN) method. While the RCG algorithm operates based on the conventional iterative method, the DNN technique relies on unsupervised deep learning. Our numerical results show that the both algorithms achieve satisfactory performance based on only statistical CSI. In addition, compared to the RCG scheme, using deep learning reduces the computational latency by more than 10 times with an almost identical total ergodic spectral efficiency achieved. These numerical results reveal that while using conventional RCG method may provide unsatisfactory latency, DNN technique shows much promise for enabling RIS in ultra reliable and low latency communications (URLLC) in the context of IIoTs
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