On the approximability of positive influence dominating set in social networks

In social networks, there is a tendency for connected users to match each other’s behaviors. Moreover, a user likely adopts a behavior, if a certain fraction of his family and friends follows that behavior. Identifying people who have the most influential effect to the others is of great advantages, especially in politics, marketing, behavior correction, and so on. Under a graph-theoretical framework, we study the positive influence dominating set (PIDS) problem that seeks for a minimal set of nodes $\mathcal{P}$ such that all other nodes in the network have at least a fraction ρ>0 of their neighbors in $\mathcal{P}$ . We also study a different formulation, called total positive influence dominating set (TPIDS), in which even nodes in $\mathcal{P}$ are required to have a fraction ρ of neighbors inside $\mathcal{P}$ . We show that neither of these problems can be approximated within a factor of (1??)lnmax{Δ,|V|^1/2}, where Δ is the maximum degree. Moreover, we provide a simple proof that both problems can be approximated within a factor lnΔ+O(1). In power-law networks, where the degree sequence follows a power-law distribution, both problems admit constant factor approximation algorithms. Finally, we present a linear-time exact algorithms for trees.     

Solving the Multidimensional Assignment Problem by a Cross-Entropy method

The Multidimensional Assignment Problem (MAP) is a higher dimensional version of the linear assignment problem, where we find tuples of elements from given sets, such that the total cost of the tuples is minimal. The MAP has many recognized applications such as data association, target tracking, and resource planning. While the linear assignment problem is solvable in polynomial time, the MAP is NP-hard. In this work, we develop a new approach based on the Cross-Entropy (CE) methods for solving the MAP. Exploiting the special structure of the MAP, we propose an appropriate family of discrete distributions on the feasible set of the MAP that allow us to design an efficient and scalable CE algorithm. The efficiency and scalability of our method are proved via several tests on large-scale problems with up to 5 dimensions and 20 elements in each dimension, which is equivalent to a 0–1 linear program with 3.2 millions binary variables and 100 constraints.     

Ethnic Minorities in Northern Mountains of Vietnam: Employment,Poverty and Income

The Italian National Statistical Office is implementing a new project for computing sub-national Purchasing Power Parities (PPPs) on a regular basis, which is based on an appropriate use of existing Consumer Price Index (CPI) data and new sources of data. Concerning the use of CPI data, in this paper the role of the Country Product Dummy (CPD) method for compiling sub-national PPPs at Basic–Heading (BH) level is analysed together with the specific issues that arise in this context, such as the need to take spatial autocorrelation among price relatives into account. The results of various experiments based on CPI data concerning 7 BHs and 19 Italian regional chief towns are presented and discussed with the aim of exploring the performance of various CPD models and analysing to what extent the type and characteristics of the data affect the estimates obtained. The statistical uncertainty associated with the ranks derived from the regional PPPs was then calculated by means of a simulation procedure. Our findings prove to be interesting and confirm that methods and CPI data for spatial comparisons are reciprocally influenced.     

A modified two-stage approach for joint modelling of longitudinal and time-to-event data

Joint models for longitudinal and time-to-event data have been applied in many different fields of statistics and clinical studies. However, the main difficulty these models have to face with is the computational problem. The requirement for numerical integration becomes severe when the dimension of random effects increases. In this paper, a modified two-stage approach has been proposed to estimate the parameters in joint models. In particular, in the first stage, the linear mixed-effects models and best linear unbiased predictorsare applied to estimate parameters in the longitudinal submodel. In the second stage, an approximation of the fully joint log-likelihood is proposed using the estimated the values of these parameters from the longitudinal submodel. Survival parameters are estimated bymaximizing the approximation of the fully joint log-likelihood. Simulation studies show that the approach performs well, especially when the dimension of random effects increases. Finally, we implement this approach on AIDS data.     

Generalised additive mixed models analysis via gammSlice

We demonstrate the use of our R package, gammSlice, for Bayesian fitting and inference in generalised additive mixed model analysis. This class of models includes generalised linear mixed models and generalised additive models as special cases. Accurate Bayesian inference is achievable via sufficiently large Markov chain Monte Carlo (MCMC) samples. Slice sampling is a key component of the MCMC scheme. Comparisons with existing generalised additive mixed model software shows that gammSlice offers improved inferential accuracy, albeit at the cost of longer computational time.     

Contents of Volume 16, 2000

Volume Contents

Contents of Volume 16, 2000     

Maximizing misinformation restriction within time and budget constraints

Online social networks have become popular media worldwide. However, they also allow rapid dissemination of misinformation causing negative impacts to users. With a source of misinformation, the longer the misinformation spreads, the greater the number of affected users will be. Therefore, it is necessary to prevent the spread of misinformation in a specific time period. In this paper, we propose maximizing misinformation restriction (\(\mathsf {MMR}\)) problem with the purpose of finding a set of nodes whose removal from a social network maximizes the influence reduction from the source of misinformation within time and budget constraints. We demonstrate that the \(\mathsf {MMR}\) problem is NP-hard even in the case where the network is a rooted tree at a single misinformation node and show that the calculating objective function is #P-hard. We also prove that objective function is monotone and submodular. Based on that, we propose an \(1{-}1/\sqrt{e}\)-approximation algorithm. We further design efficient heuristic algorithms, named \(\mathsf {PR}\)-\(\mathsf {DAG}\) to show \(\mathsf {MMR}\) in very large-scale networks.     

PeaceBuilding and Displacement in Northern Uganda: A Cross-sectional Study of Intentions to Move and Attitudes towards Former Combatants

Using data from a cross-sectional survey of internally displacedpopulations in northern Uganda, this article analyses individual-leveldeterminants of attitudes toward peacebuilding processes, includingreturning home and the reintegration of former combatants. Wefind that perceptions of social services and livelihood opportunitiesat the current place of living and at return or resettlementsites influence individuals’ decisions to move as do attitudestoward former combatants. Furthermore, we show that internallydisplaced persons are a specific group with needs and attitudesthat differ from those of others. Such empirical informationmust be taken into account for the successful development andimplementation of peace and reconstruction programmes.     

Historic and Contemporary Influences on HIV Advocacy in Vietnam

VOLUNTAS: International Journal of Voluntary and Nonprofit Organizations - HIV civil society advocacy in Vietnam is shaped by a unique fusion of historic and contemporary influences,...