共查询到20条相似文献,搜索用时 31 毫秒
1.
Dongjie Zhu Yundong Sun Xiaofang Li Haiwen Du Rongning Qu Pingping Yu Xuefeng Piao Russell Higgs Ning Cao 《计算机、材料和连续体(英文)》2020,63(3):1343-1357
Interactivity is the most significant feature of network data, especially in social
networks. Existing network embedding methods have achieved remarkable results in
learning network structure and node attributes, but do not pay attention to the multiinteraction between nodes, which limits the extraction and mining of potential deep
interactions between nodes. To tackle the problem, we propose a method called MultiInteraction heterogeneous information Network Embedding (MINE). Firstly, we introduced
the multi-interactions heterogeneous information network and extracted complex
heterogeneous relation sequences by the multi-interaction extraction algorithm. Secondly,
we use a well-designed multi-relationship network fusion model based on the attention
mechanism to fuse multiple interactional relationships. Finally, applying a multitasking
model makes the learned vector contain richer semantic relationships. A large number of
practical experiments prove that our proposed method outperforms existing methods on
multiple data sets. 相似文献
2.
To cope with the arbitrariness of the network delays, a novel method, referred to as the composite particle filter approach based on variational Bayesian (VB-CPF), is proposed herein to estimate the clock skew and clock offset in wireless sensor networks. VB-CPF is an improvement of the Gaussian mixture kalman particle filter (GMKPF) algorithm. In GMKPF, Expectation-Maximization (EM) algorithm needs to determine the number of mixture components in advance, and it is easy to generate overfitting and underfitting. Variational Bayesian EM (VB-EM) algorithm is introduced in this paper to determine the number of mixture components adaptively according to the observations. Moreover, to solve the problem of data packet loss caused by unreliable links, we propose a robust time synchronization (RTS) method in this paper. RTS establishes an autoregressive model for clock skew, and calculates the clock parameters based on the established autoregressive model in case of packet loss. The final simulation results illustrate that VB-CPF yields much more accurate results relative to GMKPF when the network delays are modeled in terms of an asymmetric Gaussian distribution. Moreover, RTS shows good robustness to the continuous and random dropout of time messages. 相似文献
3.
Community detection in social networks is a hard problem because of the size, and the need of a deep understanding of network structure and functions. While several methods with significant effort in this direction have been devised, an outstanding open problem is the unknown number of communities, it is generally believed that the role of influential nodes that are surrounded by neighbors is very important. In addition, the similarity among nodes inside the same cluster is greater than among nodes from other clusters. Lately, the global and local methods of community detection have been getting more attention. Therefore, in this study, we propose an advanced community-detection model for social networks in order to identify network communities based on global and local information. Our proposed model initially detects the most influential nodes by using an Eigen score then performs local expansion powered by label propagation. This process is conducted with the same color till nodes reach maximum similarity. Finally, the communities are formed, and a clear community graph is displayed to the user. Our proposed model is completely parameter-free, and therefore, no prior information is required, such as the number of communities, etc. We perform simulations and experiments using well-known synthetic and real network benchmarks, and compare them with well-known state-of-the-art models. The results prove that our model is efficient in all aspects, because it quickly identifies communities in the network. Moreover, it can easily be used for friendship recommendations or in business recommendation systems. 相似文献
4.
Influence maximization of temporal social networks (IMT) is a problem that aims to find the most influential set of nodes in the temporal network so that their information can be the most widely spread. To solve the IMT problem, we propose an influence maximization algorithm based on an improved K-shell method, namely improved K-shell in temporal social networks (KT). The algorithm takes into account the global and local structures of temporal social networks. First, to obtain the kernel value Ks of each node, in the global scope, it layers the network according to the temporal characteristic of nodes by improving the K-shell method. Then, in the local scope, the calculation method of comprehensive degree is proposed to weigh the influence of nodes. Finally, the node with the highest comprehensive degree in each core layer is selected as the seed. However, the seed selection strategy of KT can easily lose some influential nodes. Thus, by optimizing the seed selection strategy, this paper proposes an efficient heuristic algorithm called improved K-shell in temporal social networks for influence maximization (KTIM). According to the hierarchical distribution of cores, the algorithm adds nodes near the central core to the candidate seed set. It then searches for seeds in the candidate seed set according to the comprehensive degree. Experiments show that KTIM is close to the best performing improved method for influence maximization of temporal graph (IMIT) algorithm in terms of effectiveness, but runs at least an order of magnitude faster than it. Therefore, considering the effectiveness and efficiency simultaneously in temporal social networks, the KTIM algorithm works better than other baseline algorithms. 相似文献
5.
图模型是一种分析网络结构的有效方法,其中有向无环图由于可表示因果关系而受到广泛关注。而大量真实网络中节点的度服从幂律分布,即具有无标度特征。因此,研究了在无标度先验下,节点序已知的有向无环图结构学习问题。通过引入网络中节点度的信息和边的稀疏先验,提出罚项为 Log 型与 $l_q (0
相似文献
6.
This paper creates a LM (Levenberg-Marquardt) algorithm model which is appropriate to solve the problem about weights value of feedforward neural network. On the base of this model, we provide two applications in the oilfield production. Firstly, we simulated the functional relationships between the petrophysical and electrical properties of the rock by neural networks model, and studied oil saturation. Under the precision of data is confirmed, this method can reduce the number of experiments. Secondly, we simulated the relationships between investment and income by the neural networks model, and studied invest saturation point and income growth rate. It is very significant to guide the investment decision. The research result shows that the model is suitable for the modeling and identification of nonlinear systems due to the great fit characteristic of neural network and very fast convergence speed of LM algorithm. 相似文献
7.
Xiaoge Zhang Felix T.S. Chan Andrew Adamatzky Sankaran Mahadevan Hai Yang Zili Zhang 《国际生产研究杂志》2017,55(1):244-263
We propose an efficient bio-inspired algorithm for design of optimal supply chain networks in a competitive oligopoly markets. The firms compete in manufacture, storage and distribution of a product to several markets. Each firm aims at maximisation of its own profit by optimising the design capacity and product flow in the supply chain. We model the supply chain network as a multi-layer graph of manufacturing nodes, distribution nodes and storage centres. To optimise the network, we adopt the mechanisms of a foraging behaviour of slime mould Physarum polycephalym. First, we extend the original Physarum model to deal with networks with multiple sources and sinks. Second, we develop a novel method to solve the user equilibrium (UE) problem by exploiting the adaptivity of the Physarum model: we update the link costs according to the product flow. Third, we refer to an equivalent transformation between system optimum problem and UE problem to determine the optimal product flows and design capacities of a supply chain. At last, we present an approach to update the amount of product supplied by each firm. By comparing our solutions with that in Nagurney (2010b) on several numerical examples, we demonstrate the efficiency and practicality of the proposed method. 相似文献
8.
Graph convolutional networks (GCNs) have been developed as a general and
powerful tool to handle various tasks related to graph data. However, current methods
mainly consider homogeneous networks and ignore the rich semantics and multiple types
of objects that are common in heterogeneous information networks (HINs). In this paper,
we present a Heterogeneous Hyperedge Convolutional Network (HHCN), a novel graph
convolutional network architecture that operates on HINs. Specifically, we extract the
rich semantics by different metastructures and adopt hyperedge to model the interactions
among metastructure-based neighbors. Due to the powerful information extraction
capabilities of metastructure and hyperedge, HHCN has the flexibility to model the
complex relationships in HINs by setting different combinations of metastructures and
hyperedges. Moreover, a metastructure attention layer is also designed to allow each node
to select the metastructures based on their importance and provide potential
interpretability for graph analysis. As a result, HHCN can encode node features,
metastructure-based semantics and hyperedge information simultaneously by aggregating
features from metastructure-based neighbors in a hierarchical manner. We evaluate
HHCN by applying it to the semi-supervised node classification task. Experimental
results show that HHCN outperforms state-of-the-art graph embedding models and
recently proposed graph convolutional network models. 相似文献
9.
Oleksii Kuchaiev Tijana Milenkovi? Vesna Memi?evi? Wayne Hayes Nata?a Pr?ulj 《Journal of the Royal Society Interface》2010,7(50):1341-1354
Sequence comparison and alignment has had an enormous impact on our understanding of evolution, biology and disease. Comparison and alignment of biological networks will probably have a similar impact. Existing network alignments use information external to the networks, such as sequence, because no good algorithm for purely topological alignment has yet been devised. In this paper, we present a novel algorithm based solely on network topology, that can be used to align any two networks. We apply it to biological networks to produce by far the most complete topological alignments of biological networks to date. We demonstrate that both species phylogeny and detailed biological function of individual proteins can be extracted from our alignments. Topology-based alignments have the potential to provide a completely new, independent source of phylogenetic information. Our alignment of the protein–protein interaction networks of two very different species—yeast and human—indicate that even distant species share a surprising amount of network topology, suggesting broad similarities in internal cellular wiring across all life on Earth. 相似文献
10.
Deepak Prashar Gyanendra Prasad Joshi Sudan Jha Eunmok Yang Kwang Chul Son 《计算机、材料和连续体(英文)》2021,66(2):1529-1549
The Internet of Things (IoT) is envisioned as a network of various wireless sensor nodes communicating with each other to offer state-of-the-art solutions to real-time problems. These networks of wireless sensors monitor the physical environment and report the collected data to the base station, allowing for smarter decisions. Localization in wireless sensor networks is to localize a sensor node in a two-dimensional plane. However, in some application areas, such as various surveillances, underwater monitoring systems, and various environmental monitoring applications, wireless sensors are deployed in a three-dimensional plane. Recently, localization-based applications have emerged as one of the most promising services related to IoT. In this paper, we propose a novel distributed range-free algorithm for node localization in wireless sensor networks. The proposed three-dimensional hop localization algorithm is based on the distance error correction factor. In this algorithm, the error decreases with the localization process. The distance correction factor is used at various stages of the localization process, which ultimately mitigates the error. We simulated the proposed algorithm using MATLAB and verified the accuracy of the algorithm. The simulation results are compared with some of the well-known existing algorithms in the literature. The results show that the proposed three-dimensional error-correction-based algorithm performs better than existing algorithms. 相似文献
11.
A Neural Network Approach to Find The Cumulative Failure Distribution: Modeling and Experimental Evidence
下载免费PDF全文
![点击此处可从《Quality and Reliability Engineering International》网站下载免费的PDF全文](/ch/ext_images/free.gif)
Emanuel Federico Alsina Giacomo Cabri Alberto Regattieri 《Quality and Reliability Engineering International》2016,32(2):567-579
The failure prediction of components plays an increasingly important role in manufacturing. In this context, new models are proposed to better face this problem, and, among them, artificial neural networks are emerging as effective. A first approach to these networks can be complex, but in this paper, we will show that even simple networks can approximate the cumulative failure distribution well. The neural network approach results are often better than those based on the most useful probability distribution in reliability, the Weibull. In this paper, the performances of multilayer feedforward basic networks with different network configurations are tested, changing different parameters (e.g., the number of nodes, the learning rate, and the momentum). We used a set of different failure data of components taken from the real world, and we analyzed the accuracy of the approximation of the different neural networks compared with the least squares method based on the Weibull distribution. The results show that the networks can satisfactorily approximate the cumulative failure distribution, very often better than the least squares method, particularly in cases with a small number of available failure times. Copyright © 2015 John Wiley & Sons, Ltd. 相似文献
12.
Image processing networks have gained great success in many fields, and thus the issue of copyright protection for image processing networks has become a focus of attention. Model watermarking techniques are widely used in model copyright protection, but there are two challenges: (1) designing universal trigger sample watermarking for different network models is still a challenge; (2) existing methods of copyright protection based on trigger s watermarking are difficult to resist forgery attacks. In this work, we propose a dual model watermarking framework for copyright protection in image processing networks. The trigger sample watermark is embedded in the training process of the model, which can effectively verify the model copyright. And we design a common method for generating trigger sample watermarks based on generative adversarial networks, adaptively generating trigger sample watermarks according to different models. The spatial watermark is embedded into the model output. When an attacker steals model copyright using a forged trigger sample watermark, which can be correctly extracted to distinguish between the piratical and the protected model. The experiments show that the proposed framework has good performance in different image segmentation networks of UNET, UNET++, and FCN (fully convolutional network), and effectively resists forgery attacks. 相似文献
13.
The MTE (Mixture of Truncated Exponentials) model allows to deal with Bayesian networks containing discrete and continuous
variables simultaneously. This model offers an alternative to discretisation, since standard algorithms to compute the posterior
probabilities in the network, in principle designed for discrete variables, can be directly applied over MTE models. In this
paper, we study the problem of estimating these models from data. We propose an iterative algorithm based on least squares
approximation. The performance of the algorithm is tested both with artificial and actual data.
This work has been supported by the Spanish Ministry of Education and Science and the European Funds for Regional Development,
through projects TIC2001-2973-C05-01,02 and by UAL-CAJAMAR, project E-729/01 相似文献
14.
Brandon Alexander Alexandra Pushkar Michelle Girvan 《Journal of the Royal Society Interface》2021,18(177)
We study a simplified model of gene regulatory network evolution in which links (regulatory interactions) are added via various selection rules that are based on the structural and dynamical features of the network nodes (genes). Similar to well-studied models of ‘explosive’ percolation, in our approach, links are selectively added so as to delay the transition to large-scale damage propagation, i.e. to make the network robust to small perturbations of gene states. We find that when selection depends only on structure, evolved networks are resistant to widespread damage propagation, even without knowledge of individual gene propensities for becoming ‘damaged’. We also observe that networks evolved to avoid damage propagation tend towards disassortativity (i.e. directed links preferentially connect high degree ‘source’ genes to low degree ‘target’ genes and vice versa). We compare our simulations to reconstructed gene regulatory networks for several different species, with genes and links added over evolutionary time, and we find a similar bias towards disassortativity in the reconstructed networks. 相似文献
15.
16.
无线传感器网络的拓扑结构随着网络中节点的增加、减少和移动实时变化,为保证网络的连通性和覆盖性不被影响,拓扑控制技术所要解决的问题正是传感器节点如何更好地自组织构建全局网络拓扑.本文首先概述了四类拓扑控制算法的理论基础及算法步骤.然后,对提高网络抗毁性的两类拓扑演化算法进行了详细叙述,即无标度网络生长与构建$k$连通网络,分别构建了基于节点位置偏好的移动网络拓扑模型和基于$k$连通的节点调度优化模型.最后,分别从移动节点的引入、折中控制算法的探索、复杂网络理论的应用和传统算法与智能算法的结合这四方面对拓扑控制算法的前景进行了阐述. 相似文献
17.
18.
Complex technological networks designed for distribution of some resource or commodity are a pervasive feature of modern society. Moreover, the dependence of our society on modern technological networks constantly grows. As a result, there is an increasing demand for these networks to be highly reliable in delivering their service. As a consequence, there is a pressing need for efficient computational methods that can quantitatively assess the reliability of technological networks to enhance their design and operation in the presence of uncertainty in their future demand, supply and capacity. In this paper, we propose a stochastic framework for quantitative assessment of the reliability of network service, formulate a general network reliability problem within this framework, and then show how to calculate the service reliability using Subset Simulation, an efficient Markov chain Monte Carlo method that was originally developed for estimating small failure probabilities of complex dynamic systems. The efficiency of the method is demonstrated with an illustrative example where two small-world network generation models are compared in terms of the maximum-flow reliability of the networks that they produce. 相似文献
19.
Construction of Probabilistic Boolean Networks from a Prescribed Transition Probability Matrix: A Maximum Entropy Rate Approach
下载免费PDF全文
![点击此处可从《East Asian journal on applied mathematics.》网站下载免费的PDF全文](/ch/ext_images/free.gif)
Xi Chen Wai-Ki Ching Xiao-Shan Chen Yang Cong & Nam-Kiu Tsing 《East Asian journal on applied mathematics.》2011,1(2):132-154
Modeling genetic regulatory networks is an important problem in genomic
research. Boolean Networks (BNs) and their extensions Probabilistic Boolean Networks
(PBNs) have been proposed for modeling genetic regulatory interactions. In a PBN, its
steady-state distribution gives very important information about the long-run behavior
of the whole network. However, one is also interested in system synthesis which requires
the construction of networks. The inverse problem is ill-posed and challenging, as there
may be many networks or no network having the given properties, and the size of the
problem is huge. The construction of PBNs from a given transition-probability matrix
and a given set of BNs is an inverse problem of huge size. We propose a maximum
entropy approach for the above problem. Newton’s method in conjunction with the
Conjugate Gradient (CG) method is then applied to solving the inverse problem. We
investigate the convergence rate of the proposed method. Numerical examples are also
given to demonstrate the effectiveness of our proposed method. 相似文献
20.
Calculating the semantic similarity of two sentences is an extremely
challenging problem. We propose a solution based on convolutional neural networks
(CNN) using semantic and syntactic features of sentences. The similarity score between
two sentences is computed as follows. First, given a sentence, two matrices are
constructed accordingly, which are called the syntax model input matrix and the semantic
model input matrix; one records some syntax features, and the other records some
semantic features. By experimenting with different arrangements of representing the
syntactic and semantic features of the sentences in the matrices, we adopt the most
effective way of constructing the matrices. Second, these two matrices are given to two
neural networks, which are called the sentence model and the semantic model,
respectively. The convolution process of the neural networks of the two models is carried
out in multiple perspectives. The outputs of the two models are combined as a vector,
which is the representation of the sentence. Third, given the representation vectors of two
sentences, the similarity score of these representations is computed by a layer in the
CNN. Experiment results show that our algorithm (SSCNN) surpasses the performance
MPCPP, which noticeably the best recent work of using CNN for sentence similarity
computation. Comparing with MPCNN, the convolution computation in SSCNN is
considerably simpler. Based on the results of this work, we suggest that by further
utilization of semantic and syntactic features, the performance of sentence similarity
measurements has considerable potentials to be improved in the future. 相似文献