Specific Diffusion Model Research Articles

Research and Analysis of Influence Maximization Techniques in Online Network Communities Based on Social Big Data

Recent years, many online network communities, such as Facebook, Twitter, Tik Tok, Weibo, etc., have developed rapidly and become the bridge connecting physical social world and virtual cyberspace. Online network communities store a large number of social relationships and interactions between users. How to analyze diffusion of influence from these massive social data has become a research hotspot in the applications of big data mining in online network communities. A core issue in the study of influence diffusion is influence maximization. Influence maximization refers to selecting a few nodes in a social network as seeds, so as to maximize influence spread of seed nodes under a specific diffusion model. Focusing on two core aspects of influence maximization, i.e., models and algorithms, this paper summarizes the main achievements of research on influence maximization in the computer field in recent years. Finally, this paper briefly discusses issues, challenges and future research directions in the research and application of influence maximization.

RLIM: representation learning method for influence maximization in social networks

A core issue in influence propagation is influence maximization, which aims to find a set of nodes that maximize influence spread by adopting a specific information diffusion model. The limitation of the existing algorithms is they excessively depend on the information diffusion model and randomly set the propagation ability. Therefore, most algorithms are difficult to apply in large-scale social networks. A method to solve the problem is neural network architecture. Based on the architecture, the paper proposes Representation Learning for Influence Maximization (RLIM) algorithm. The algorithm consists of three main parts: the influence cascade of each source node is the premise; the multi-task deep learning neural network to classify influenced nodes and predict propagation ability is the fundamental bridge; the prediction model applying to the influence maximization problem by the greedy strategy is the purpose. Furthermore, the experimental results show that the RLIM algorithm has greater influence spread than the state-of-the-art algorithms in different online social network datasets, and the information diffusion is more accurate.

A Hierarchical Generative Embedding Model for Influence Maximization in Attributed Social Networks

Nowadays, we use social networks such as Twitter, Facebook, WeChat and Weibo as means to communicate with each other. Social networks have become so indispensable in our everyday life that we cannot imagine what daily life would be like without social networks. Through social networks, we can access friends’ opinions and behaviors easily and are influenced by them in turn. Thus, an effective algorithm to find the top-K influential nodes (the problem of influence maximization) in the social network is critical for various downstream tasks such as viral marketing, anticipating natural hazards, reducing gang violence, public opinion supervision, etc. Solving the problem of influence maximization in real-world propagation scenarios often involves estimating influence strength (influence probability between two nodes), which cannot be observed directly. To estimate influence strength, conventional approaches propose various humanly devised rules to extract features of user interactions, the effectiveness of which heavily depends on domain expert knowledge. Besides, they are often applicable for special scenarios or specific diffusion models. Consequently, they are difficult to generalize into different scenarios and diffusion models. Inspired by the powerful ability of neural networks in the field of representation learning, we designed a hierarchical generative embedding model (HGE) to map nodes into latent space automatically. Then, with the learned latent representation of each node, we proposed a HGE-GA algorithm to predict influence strength and compute the top-K influential nodes. Extensive experiments on real-world attributed networks demonstrate the outstanding superiority of the proposed HGE model and HGE-GA algorithm compared with the state-of-the-art methods, verifying the effectiveness of the proposed model and algorithm.

Computation of time probability distributions for the occurrence of uncertain future events

The determination of the time at which an event may take place in the future is a well-studied problem in a number of science and engineering disciplines. Indeed, for more than fifty years, researchers have tried to establish adequate methods to characterize the behaviour of dynamic systems in time and implement predictive decision-making policies. Most of these efforts intend to model the evolution in time of nonlinear dynamic systems in terms of stochastic processes; while defining the occurrence of events in terms of first-passage time problems with thresholds that could be either deterministic or probabilistic in nature. The random variable associated with the occurrence of such events has been determined in closed-form for a variety of specific continuous-time diffusion models, being most of the available literature motivated by physical phenomena. Unfortunately, literature is quite limited in terms of rigorous studies related to discrete-time stochastic processes, despite the tremendous amount of digital information that is currently being collected worldwide. In this regard, this article provides a mathematically rigorous formalization for the problem of computing the probability of occurrence of uncertain future events in both discrete- and continuous-time stochastic processes, by extending the notion of thresholds in first-passage time problems to a fully probabilistic notion of “uncertain events” and “uncertain hazard zones”. We focus on discrete-time applications by showing how to compute those probability measures and validate the proposed framework by comparing to the results obtained with Monte Carlo simulations; all motivated by the problem of fatigue crack growth prognosis.

A patent-based method for monitoring the development of technological innovations based on knowledge diffusion

PurposeThe purpose of this paper is to investigate the diffusion patterns of knowledge in inspiring technological innovations and to enable monitoring development trends of technological innovations based on patent data analysis, thus, to manage knowledge wisely to innovate.Design/methodology/approachThe notion of knowledge innovation potential (KIP) is proposed to measure the innovativeness of knowledge by the cumulative number of patents originated from its inspiration. KIP calculating formula is regressed in forms of two specific diffusion models by conducting a series of empirical studies with the patent-based indicators involving forward and backward citation numbers to reveal knowledge managing strategies regarding innovative activities.FindingsTwo specific diffusion models for regressing KIP formula are compared by empirical studies with the result indicating the Gompertz model has higher accuracy than the Logistic model to describe the developing curve of technological innovations. Moreover, the analysis of patent-based indicators over diffusion stages also revealed that patents applied at earlier diffusion stages normally has higher forward citation numbers indicating higher innovativeness meanwhile the patents applied at the latter stages usually requiring more knowledge inflows observed by their larger non-patent citation and backward citation amounts.Originality/valueAlthough there is a large body of literature concerning knowledge-based technological innovation, there still room for discussing the mechanism of how knowledge diffuses and inspired knowledge. To the best of authors' knowledge, this study is the first attempt to quantitate the innovativeness of knowledge in technological innovation from the knowledge diffusion perspective with findings to support rational knowledge management related to innovation activities.

Deep Reinforcement Learning-Based Approach to Tackle Topic-Aware Influence Maximization

Motivated by the application of viral marketing, the topic-aware influence maximization (TIM) problem has been proposed to identify the most influential users under given topics. In particular, it aims to find k seeds (users) in social network G, such that the seeds can maximize the influence on users under the specific query topics and diffusion model such as independent cascade (IC) or linear threshold (LT).This problem has been proved to be NP-hard, and most of the proposed techniques suffer from the efficiency issue due to the lack of generalization. Even worse, the design of these algorithms requires significant specialized knowledge which is hard to be understood and implemented. To overcome these issues, this paper aims to learn a generalized heuristic framework to solve TIM problems by meta-learning. To this end, we first propose two topic-aware social influence propagation models based on IC and LT model, respectively, which is conducive to better advertising injections. We then encode the feature of each node by a vector and introduce a model, called deep influence evaluation model , to evaluate the user influence under different circumstances. Based on this model, we can construct the solution according to the influence evaluations efficiently, rather than spending a high cost to compute the exact influence by considering the complex graph structure. We conducted experiments on generated graph instances and real-world social networks. The results show the superiority in performance and comparable quality of our framework.

Scalar diffusion-MRI measures invariant to acquisition parameters: A first step towards imaging biomarkers

An imaging biomarker is a biologic feature in an image that is relevant to a patient's diagnosis or prognosis. In order to qualify as a biomarker, a measure must be robust and reproducible. However, the usual scalar measures derived from diffusion tensor imaging are known to be highly dependent on the variation of the acquisition parameters, which prevents their possible use as biomarkers. In this work, we propose a new set of quantitative measures based on diffusion magnetic resonance imaging from single-shell acquisitions that are designed to be robust to the variations of several acquisition parameters (number of gradient directions, b-value and SNR) while keeping a high discrimination power on differences in the diffusion characteristics of the tissue.These new scalar measures are analytically obtained from a generic diffusion function that does not require the calculation of a diffusion tensor. This way, on one hand, we avoid the use of a specific diffusion model and, on the other hand, we make easier the statistical characterization of the measures. Accordingly, the analysis of the measures bias is carried out and it is used to minimize their dependency with respect to the acquisition noise for different SNRs. The robustness and discrimination power of the measures are tested for different number of gradients, b-values and SNRs using a realistic phantom and three real datasets: (1) 13 control subjects and different acquisition parameters; (2) a public data set from a single subject acquired using multiple shells and (3) 32 schizophrenia patients and 32 age and sex-matched healthy controls with a varying number of gradient directions.The proposed quantitative measures exhibit low variability to the changes of the acquisition parameters, while at the same time they preserve a discrimination power that is able to detect significant changes in the anisotropy of the diffusion.

A general method for obtaining diffusion coefficients by inversion of measured torque from diffusion experiments under small amplitude oscillatory shear

A numerical approach based on Tikhonov regularization is developed to invert torque curves from time-dependent small amplitude oscillatory shear (SAOS) experiments in which diffusion occurs to determine the diffusion coefficient. Diffusion of a solvent into a polymer melt for example causes the measured torque to decrease over time and is thus dependent on diffusion kinetics and the concentration profile. Our numerical approach provides a general method for retrieving local viscosity profiles during diffusion with reasonable accuracy, depending only on the linear viscoelastic constitutive equation and a general power law dependency of the diffusion process on time. This approach also allows us to identify the type of diffusion (Fickian, pseudo-Fickian, anomalous, and glassy) and estimate the diffusion coefficient without the a priori identification of a specific diffusion model. Retrieving local viscosity profiles from torque measurements in the presence of a concentration gradient is an ill-posed problem of the second type and requires Tikhonov regularization. The robustness of our approach is demonstrated using a number of virtual experiments, with data sets from Fickian and non-Fickian theoretical concentration and torque profiles as well as real experimental data.

CONCENTRATED SOLAR POWER (CSP) INNOVATION ANALYSIS IN SOUTH AFRICA

South Africa aims to generate 42 per cent of its electricity from renewable energy technology sources by 2030. Concentrating solar power (CSP) is one of the major renewable energy technologies that have been prioritised by South Africa, given the abundant solar resources available in the region. Seven CSP plants have been, or are being, built; three of them are already connected to the national grid. However, the impacts of this technology on South African research, development, and innovation have not been investigated to date. This paper thus analyses the CSP technologies in South Africa in terms of the existing technology adoption models and diffusion strategies, used by government and its agencies, to improve the development and deployment of these technologies. It is found that CSP has been treated generally like other renewable energy technologies through the Renewable Energy Independent Power Producer Procurement Programme (REIPPPP), although a tariff plan for CSP plants of the future has been made. No specific technology diffusion or adoption model for CSP was found; so this paper explores how it can be developed.

Speeding-up node influence computation for huge social networks

We address the problem of efficiently estimating the influence degree for all the nodes simultaneously in the network under the SIR setting. The proposed approach is a further improvement over the existing work of the bond percolation process which was demonstrated to be very effective, i.e., three orders of magnitude faster than direct Monte Carlo simulation, in approximately solving the influence maximization problem. We introduce two pruning techniques which improve computational efficiency by an order of magnitude. This approach is generic and can be instantiated to any specific diffusion model. It does not require any approximations or assumptions to the model that were needed in the existing approaches. We demonstrate its effectiveness by extensive experiments on two large real social networks. Main finding includes that different network structures have different epidemic thresholds and the node influence can identify influential nodes that the existing centrality measures cannot. We analyze how the performance changes when the network structure is systematically changed using synthetically generated networks and identify important factors that affect the performance.

Estimation and Inference of a Time-dependent Second-order Diffusion Model with Application to Financial Time Series

A specific second-order diffusion model which has been found to be promising in analyzing financial time series is further extended to incorporate the time factor into the coefficients in the drift and diffusion functions. A two-step estimation approach is proposed to estimate the time-dependent coefficients in the drift and diffusion functions. A generalized likelihood ratio test is formulated to identify whether the coefficients vary actually with time. Some simulations are conducted to assess the performance of the estimation and testing approaches. Two stock price series are analyzed to demonstrate the applications of the proposed estimation and testing methods.

A Framework for Linear and Non-Linear Registration of Diffusion-Weighted MRIs Using Angular Interpolation

Registration of diffusion-weighted magnetic resonance images (DW-MRIs) is a key step for population studies, or construction of brain atlases, among other important tasks. Given the high dimensionality of the data, registration is usually performed by relying on scalar representative images, such as the fractional anisotropy (FA) and non-diffusion-weighted (b0) images, thereby ignoring much of the directional information conveyed by DW-MR datasets itself. Alternatively, model-based registration algorithms have been proposed to exploit information on the preferred fiber orientation(s) at each voxel. Models such as the diffusion tensor or orientation distribution function (ODF) have been used for this purpose. Tensor-based registration methods rely on a model that does not completely capture the information contained in DW-MRIs, and largely depends on the accurate estimation of tensors. ODF-based approaches are more recent and computationally challenging, but also better describe complex fiber configurations thereby potentially improving the accuracy of DW-MRI registration. A new algorithm based on angular interpolation of the diffusion-weighted volumes was proposed for affine registration, and does not rely on any specific local diffusion model. In this work, we first extensively compare the performance of registration algorithms based on (i) angular interpolation, (ii) non-diffusion-weighted scalar volume (b0), and (iii) diffusion tensor image (DTI). Moreover, we generalize the concept of angular interpolation (AI) to non-linear image registration, and implement it in the FMRIB Software Library (FSL). We demonstrate that AI registration of DW-MRIs is a powerful alternative to volume and tensor-based approaches. In particular, we show that AI improves the registration accuracy in many cases over existing state-of-the-art algorithms, while providing registered raw DW-MRI data, which can be used for any subsequent analysis.

Reconstruction of scattered data in fetal diffusion MRI

In this paper we present a method for reconstructing diffusion-weighted MRI data on regular grids from scattered data. The proposed method has the advantage that no specific diffusion model needs to be assumed. Previous work assume the tensor model, but this is not suitable under certain conditions like intravoxel orientational heterogeneity (IVOH). Data reconstruction is particularly important when studying the fetal brain in utero, since registration methods applied for movement and distortion correction produce scattered data in spatial and diffusion domains. We propose the use of a groupwise registration method, and a dual spatio-angular interpolation by using radial basis functions (RBF). Leave-one-out experiments performed on adult data showed a high accuracy of the method. The application to fetal data showed an improvement in the quality of the sequences according to objective criteria based on fractional anisotropy (FA) maps, and differences in the tractography results.

Optimum advertising policy over time for subscriber service innovations in the presence of service cost learning and customers’ disadoption

On the theoretical side, this paper characterizes qualitatively optimal advertising policy for new subscriber services. A monopolistic market is analyzed first for which customers’ disadoption, discounting of future profits streams and a service cost learning curve are allowed. After characterizing the optimal policy for a general diffusion model, the results pertaining to a specific diffusion model for which advertising affects the coefficient of innovation that incorporates the disadoption rate are reported. The results of the theoretical research show that the advertising policy of the service firm in the presence of customers’ disadoption could be very different from the same when disadoption is ignored. On the empirical side, four alternative diffusion models are estimated and their predictive powers using a one-step-ahead forecasting procedure compared. The diffusion data analyzed are related to the Canadian cable TV industry. Empirical research findings suggest that the specific diffusion model considered above is not only of theoretical appeal but also of major empirical relevance. The analytical findings of the study are documented in six theoretical propositions for which proofs are provided in a separate Appendix. The results of a related numerical experiment together with the analytical findings pertaining to the competitive role of advertising are included. Managerial implications of the study together with directions for future research are also discussed.

Speed of reaction diffusion in embryogenesis

Reaction diffusion systems have been proposed as mechanisms for patterning during many stages of embryonic development. While much attention has been focused on the study of the steady state patterns formed and the robustness of pattern selection, much less is known about the time scales required for pattern formation. Studies of gradient formation by the diffusion of a single morphogen from a localized source have shown that patterning can occur on realistic time scales over distances of a millimeter or less. Reaction diffusion has the potential to give rise to patterns on a faster time scale, since all points in the domain can act as sources of morphogen. However, the speed at which patterning can occur has hitherto not been explored in depth. In this paper, we investigate this issue in specific reaction diffusion models and address the question of whether patterning via reaction diffusion is fast enough to be applicable to morphogenesis.

Analysis of transient permeation fluxes into and out of membranes for adsorption measurements

A relationship between the integrated fluxes into and out of a membrane following a positive or negative step change in feed concentration was derived. This analysis allows adsorption isotherms in the transport pathways through membranes to be determined from transient permeation responses to step concentration changes in the feed without measuring the retentate response. For Fickian diffusion through a membrane with Langmuir adsorption and zero coverage at the permeate boundary, the difference between the time-integrated flux into and flux out of the membrane is shown to be three times the time-integrated difference between the steady-state flux and the flux out. For Maxwell–Stefan diffusion, this ratio of integrated flux differences is 3 at low coverages and decreases towards 2 at saturation coverage. Mass transfer resistance at the permeate boundary increases the Fickian ratio above 3, and the ratio increases with decreasing Sherwood number. The ratio of integrated flux differences is shown to be identical to the steady-state replenishment time divided by the time lag. Thus, the ratio can be calculated directly from the steady-state concentration profile and the concentration-dependent diffusion coefficient. Surface diffusion through zeolite membranes was analyzed to demonstrate the calculation of the flux relationship for specific adsorption and diffusion models, but the method developed can be applied to membrane permeation in general.

Numerical Solutions to Some Optimal Control Problems Arising fromInnovation Diffusion

In this paper we propose a numerical approach for the solution of some optimal control problems arising in the field of marketing decision models. In particular, we account for a specific innovation diffusion model. A numerical approach may be useful to investigate some features of state variables and parameters of interest. The discrete problem is solved by the Simulated Annealing method and the resulting numerical scheme is applied to some test cases.

The effect of liquid push out of the material capillaries under sulfate ion diffusion in cement composites

The paper treats complex phenomena that accompany the diffusion of sulfate ions into cement paste or systems. The ion distribution within the material was studied by designing a specific diffusion model. The model accounts for two phenomena: capillary filling with products of the chemical reactions and the subsequent liquid push out of the capillary. The approach allows to quantify the concentration of free ions having penetrated the cement stone and that of chemically reacted ions, and to assess the liquid push out. Experimental data are also presented.

Application of electrical conductivity for determining the chemical diffusivity in transition-metal compounds

Electrical conductivity is a very convenient means for determining chemical diffusivity in transition metal compounds. Misinterpretation may however occur about its actual significance in such nonequilibrium measurements. A detailed analysis is provided here about the application of electrical conductivity and the development of specific diffusion models in regard to Wagner's theory. One major exception is the simultaneous diffusion of both cations and anions within their respective sublattices. Results on two typical oxides, TiO 2 and CoO, are briefly discussed.