Efficient algorithms for influence maximization in social networks

In recent years, due to the surge in popularity of social-networking web sites, considerable interest has arisen regarding influence maximization in social networks. Given a social network structure, the problem of influence maximization is to determine a minimum set of nodes that could maximize the spread of influences. With a large-scale social network, the efficiency and practicability of such algorithms are critical. Although many recent studies have focused on the problem of influence maximization, these works in general are time-consuming when a social network is large-scale. In this paper, we propose two novel algorithms, CDH-Kcut and Community and Degree Heuristic on Kcut/SHRINK, to solve the influence maximization problem based on a realistic model. The algorithms utilize the community structure, which significantly decreases the number of candidates of influential nodes, to avoid information overlap. The experimental results on both synthetic and real datasets indicate that our algorithms not only significantly outperform the state-of-the-art algorithms in efficiency but also possess graceful scalability.     

Utility maximization for communication networks with multipath routing

In this paper, we study utility maximization problems for communication networks where each user (or class) can have multiple alternative paths through the network. This type of multi-path utility maximization problems appear naturally in several resource allocation problems in communication networks, such as the multi-path flow control problem, the optimal quality-of-service (QoS) routing problem, and the optimal network pricing problem. We develop a distributed solution to this problem that is amenable to online implementation. We analyze the convergence of our algorithm in both continuous-time and discrete-time, and with and without measurement noise. These analyses provide us with guidelines on how to choose the parameters of the algorithm to ensure efficient network control.     

Online capacity maximization in wireless networks

In this paper we study a dynamic version of capacity maximization in the physical model of wireless communication. In our model, requests for connections between pairs of points in Euclidean space of constant dimension d arrive iteratively over time. When a new request arrives, an online algorithm needs to decide whether or not to accept the request and to assign one out of k channels and a transmission power to the request. Accepted requests must satisfy constraints on the signal-to-interference-plus-noise (SINR) ratio. The objective is to maximize the number of accepted requests. Using competitive analysis we study algorithms using distance-based power assignments, for which the power of a request relies only on the distance between the points. Such assignments are inherently local and particularly useful in distributed settings. We first focus on the case of a single channel. For request sets with spatial lengths in [1,Δ] and duration in [1,Γ] we derive a lower bound of Ω(Γ?Δ ^d/2) on the competitive ratio of any deterministic online algorithm using a distance-based power assignment. Our main result is a near-optimal deterministic algorithm that is O(Γ?Δ^(d/2)+ε )-competitive, for any constant ε>0. Our algorithm for a single channel can be generalized to k channels. It can be adjusted to yield a competitive ratio of O(k?Γ ^1/k′?Δ^(d/2k″)+ε ) for any factorization (k′,k″) such that k′?k″=k. This illustrates the effectiveness of multiple channels when dealing with unknown request sequences. In particular, for Θ(log?Γ?log?Δ) channels this yields an O(log?Γ?log?Δ)-competitive algorithm. Additionally, we show how this approach can be turned into a randomized algorithm, which is O(log?Γ?log?Δ)-competitive even for a single channel.     

Multi-stage opinion maximization in social networks

Neural Computing and Applications - Opinion maximization is a crucial optimization approach, which can be used in preventative health, such as heart disease, stroke or diabetes. The key issue of...     

Hybridization of the probabilistic neural networks with feed-forward neural networks for forecasting

Feed-forward neural networks (FFNNs) are among the most important neural networks that can be applied to a wide range of forecasting problems with a high degree of accuracy. Several large-scale forecasting competitions with a large number of commonly used time series forecasting models conclude that combining forecasts from more than one model often leads to improved performance, especially when the models in the ensemble are quite different. In the literature, several hybrid models have been proposed by combining different time series models together. In this paper, in contrast of the traditional hybrid models, a novel hybridization of the feed-forward neural networks (FFNNs) is proposed using the probabilistic neural networks (PNNs) in order to yield more accurate results than traditional feed-forward neural networks. In the proposed model, the estimated values of the FFNN models are modified based on the distinguished trend of their residuals and optimum step length, which are respectively yield from a probabilistic neural network and a mathematical programming model. Empirical results with three well-known real data sets indicate that the proposed model can be an effective way in order to construct a more accurate hybrid model than FFNN models. Therefore, it can be applied as an appropriate alternative model for forecasting tasks, especially when higher forecasting accuracy is needed.     

Bayesian selective combination of multiple neural networks for improving long-range predictions in nonlinear process modelling

A Bayesian selective combination method is proposed for combining multiple neural networks in nonlinear dynamic process modelling. Instead of using fixed combination weights, the probability of a particular network being the true model is used as the combination weight for combining that network. The prior probability is calculated using the sum of squared errors of individual networks on a sliding window covering the most recent sampling times. A nearest neighbour method is used for estimating the network error for a given input data point, which is then used in calculating the combination weights for individual networks. Forward selection and backward elimination are used to select the individual networks to be combined. In forward selection, individual networks are gradually added into the aggregated network until the aggregated network error on the original training and testing data sets cannot be further reduced. In backward elimination, all the individual networks are initially aggregated and some of the individual networks are then gradually eliminated until the aggregated network error on the original training and testing data sets cannot be further reduced. Application results demonstrate that the proposed techniques can significantly improve model generalisation and perform better than aggregating all the individual networks.     

Combining belief networks and neural networks for scenesegmentation

We are concerned with the problem of image segmentation, in which each pixel is assigned to one of a predefined finite number of labels. In Bayesian image analysis, this requires fusing together local predictions for the class labels with a prior model of label images. Following the work of Bouman and Shapiro (1994), we consider the use of tree-structured belief networks (TSBNs) as prior models. The parameters in the TSBN are trained using a maximum-likelihood objective function with the EM algorithm and the resulting model is evaluated by calculating how efficiently it codes label images. A number of authors have used Gaussian mixture models to connect the label field to the image data. We compare this approach to the scaled-likelihood method of Smyth (1994) and Morgan and Bourlard (1995), where local predictions of pixel classification from neural networks are fused with the TSBN prior. Our results show a higher performance is obtained with the neural networks. We evaluate the classification results obtained and emphasize not only the maximum a posteriori segmentation, but also the uncertainty, as evidenced e.g., by the pixelwise posterior marginal entropies. We also investigate the use of conditional maximum-likelihood training for the TSBN and find that this gives rise to improved classification performance over the ML-trained TSBN     

A new algorithm for the influence maximization problem in dynamic networks or traffic sensor networks

Influence spread is one of the key problems in complex networks, and the results of influence maximization problem (IMP) based on dynamic networks are less. In this paper, we discuss the dynamic IMP and describe the dynamic independent cascade model (DICM) and the dynamic linear threshold model (DLTM). We also conclude that IMP based on DICM and DLTM is NP-Hard. To solve the IMP, we present an improved greedy algorithm that is validated based on four datasets with different sizes. Our findings indicate that, compared with the HT algorithm, the size of the influence spread of our algorithm has an obvious advantage, and time efficiency is better than that of the HT algorithm.     

Optimal energy allocation in heterogeneous wireless sensor networks for lifetime maximization

We consider the problem of optimal energy allocation and lifetime maximization in heterogeneous wireless sensor networks. We construct a probabilistic model for heterogeneous wireless sensor networks where sensors can have different sensing range, different transmission range, different energy consumption for data sensing, and different energy consumption for data transmission, and the stream of data sensed and transmitted from a sensor and the stream of data relayed by a sensor to a base station are all treated as Poisson streams. We derive the probability distribution and the expectation of the number of data transmissions during the lifetime of each sensor and the probability distribution and the expectation of the lifetime of each sensor. In all these analysis, energy consumption of data sensing and data transmission and data relay are all taken into consideration. We develop an algorithm to find an optimal initial energy allocation to the sensors such that the network lifetime in the sense of the identical expected sensor lifetime is maximized. We show how to deal with a large amount of energy budget that may cause excessive computational time by developing accurate closed form approximate expressions of sensor lifetime and network lifetime and optimal initial energy allocation. We derive the expected number of working sensors at any time. Based on such results, we can find the latest time such that the expected number of sensors that are still functioning up to that time is above certain threshold.     

Explanation facility for neural networks

This paper give a methodology, PROLOG code, as well as an example of an explanation facility EN applicable to most neural networks. It involves How?, Why? and TRACE facilities, and is based on a general explanation degree calculation in a multilayer neural network, as well as on input node characterization grammars for synthesis of explanation text.     

VLSI architectures for neural networks

An introduction to neural networks and neural information processing is provided. Neurocomputers are discussed, focusing on how their design exploits the architectural properties of VLSI circuits. General-purpose and special-purpose neurocomputer developments throughout the world are examined. As illustration, and to put European developments in perspective, some of the important projects in the United States and Japan are described. European research is then discussed in greater detail     

Scalable influence maximization for independent cascade model in large-scale social networks

Influence maximization, defined by Kempe et al. (SIGKDD 2003), is the problem of finding a small set of seed nodes in a social network that maximizes the spread of influence under certain influence cascade models. The scalability of influence maximization is a key factor for enabling prevalent viral marketing in large-scale online social networks. Prior solutions, such as the greedy algorithm of Kempe et al. (SIGKDD 2003) and its improvements are slow and not scalable, while other heuristic algorithms do not provide consistently good performance on influence spreads. In this article, we design a new heuristic algorithm that is easily scalable to millions of nodes and edges in our experiments. Our algorithm has a simple tunable parameter for users to control the balance between the running time and the influence spread of the algorithm. Our results from extensive simulations on several real-world and synthetic networks demonstrate that our algorithm is currently the best scalable solution to the influence maximization problem: (a) our algorithm scales beyond million-sized graphs where the greedy algorithm becomes infeasible, and (b) in all size ranges, our algorithm performs consistently well in influence spread—it is always among the best algorithms, and in most cases it significantly outperforms all other scalable heuristics to as much as 100–260% increase in influence spread.     

Three-hop velocity attenuation propagation model for influence maximization in social networks

World Wide Web - In the study of influence maximization in social networks, the speed of information dissemination decreases with increasing time and distance. The investigation of the...     

Network lifetime maximization for time-sensitive data gathering in wireless sensor networks

Energy-constrained sensor networks have been widely deployed for environmental monitoring and security surveillance purposes. Since sensors are usually powered by energy-limited batteries, in order to prolong the network lifetime, most existing research focuses on constructing a load-balanced routing tree rooted at the base station for data gathering. However, this may result in a long routing path from some sensors to the base station. Motivated by the need of some mission-critical applications that require all sensed data to be received by the base station with minimal delay, this paper aims to construct a routing tree such that the network lifetime is maximized while keeping the routing path from each sensor to the base station minimized. This paper shows that finding such a tree is NP-hard. Thus a novel heuristic called top-down algorithm is presented, which constructs the routing tree layer by layer such that each layer is optimally extended, using a network flow model. A distributed refinement algorithm is then devised that dramatically improves on the load balance for the routing tree produced by the top-down algorithm. Finally, extensive simulations are conducted. The experimental results show that the top-down algorithm with balance-refinement delivers a shortest routing tree whose network lifetime achieves around 85% of the optimum.     

Profit maximization and risk minimization in semi-Markovian networks

The problem of profit maximization and risk minimization under stationary conditions is considered for multichannel semi-Markovian networks. Systems of integral equations that describe data handling and profit accumulation in the network are subjected to asymptotic analysis. The objective functions of the optimization problems are expressed explicitly in terms of network parameters. __________ Translated from Kibernetika i Sistemnyi Analiz, No. 2, pp. 65–79, March–April 2007.     

Application of a multi-layered neural network to system identification

A study on the application of the neural network method to estimate chemical oxygen demand (COD) in a wastewater treatment process is introduced after presenting the back propagation algorithm, one of the most typical algorithms of neural networks, in detail. Several types of simulation have been tried in order to select suitable neural network parameters. The results of simulation to estimate real sampling COD data for a specific real-world wastewater treatment process shows that our method is promising.     

Beyond graph neural networks with lifted relational neural networks

Machine Learning - We introduce a declarative differentiable programming framework, based on the language of Lifted Relational Neural Networks, where small parameterized logic programs are used to...