A novel probabilistic clustering model for heterogeneous networks

Finding different types of communities has become a research hot spot in network science. Plenty of the real-world systems containing different types of objects and relationships can be perfectly described as the heterogeneous networks. However, most of the current research on community detection is applied for the homogeneous networks, while there is no effective function to quantify the quality of the community structure in heterogeneous networks. In this paper, we first propose the null model with the same heterogeneous node degree distribution of the original heterogeneous networks. The probability of there being an edge between two nodes is given to build the modularity function of the heterogeneous networks. Based on our modularity function, a fast algorithm of community detection is proposed for the large scale heterogeneous networks. We use the algorithm to detect the communities in the real-world twitter event networks. The experimental results show that our method perform better than other exciting algorithms and demonstrate that the modularity function of the heterogeneous networks is an effective parameter that can be used to quantify the quality of the community structure in heterogeneous networks.

Scarcity of bandwidth for fixed allocation of frequency and also current growth trends in wireless communications shows that Cognitive Radio Networks (CRNs) are followed strictly. Fluctuating nature of the radio spectrum with a variety of quality services which needs causes some challenges in CRNs, such as spectrum sharing. In many previous works, the problem of performance loss is seen due to the sudden presence of primary user. In such a situation, secondary user has to vacate bandwidth at the moment. This often leads to long wait for secondary user on access the channel. However, Spectrum Pooling concept is used with the ability of secondary utility for licensed frequency band, without reducing quality of service and without requiring new hardware. In this paper, a fair queuing algorithm with weighting and prioritization between different users is recommended. This type of queuing model with weighted fair queuing policy is efficient to share the bandwidth among systems with different priority levels. The proposed algorithm offers shared use of spectrum to invest a fair sharing of bandwidth between cognitive users. The proposed model involves a different service rates for unlicensed users and can be used as an acceptable model in heterogeneous networks. By the use of analytical and simulation results, we can achieve a desirable level of performance and an improvement in fairness.

Recently, there is a surge of interests on heterogeneous information network analysis. Although evaluating the importance of objects has been well studied in homogeneous networks, it is not yet exploited in heterogeneous networks. In this paper, we study the ranking problem in heterogeneous networks and propose the HRank method to evaluate the importance of multiple types of objects and meta paths. A constrained meta path is proposed to subtly capture the rich semantics in heterogeneous networks. Since the importance of objects depends upon the meta paths in heterogeneous networks, HRank develops a path based random walk process. Furthermore, HRank can simultaneously determine the importance of objects and meta paths through applying the tensor analysis. Experiments on three real datasets show that HRank can effectively evaluate the importance of objects and paths together. Moreover, the constrained meta path shows its potential on mining subtle semantics by obtaining more accurate ranking results.

With the development of the wireless communication technology and the sensor technology, the wireless sensor network (WSN) has been used in many applications. However, WSNs suffer from some inherent weaknesses because of restricted communication and hardware capabilities. To achieve essentially secure communication in WSNs, a few of key management models have been proposed since it is the crucial important building block for all security goals in WSNs. Recently, Alagheband and Aref proposed a signcryption scheme and used it to construct a dynamic key management model for hierarchical heterogeneous sensor networks. They also proved that their signcryption scheme is provably secure if the elliptic curve discrete logarithm problem is infeasible. Unfortunately, by giving concrete attacks, we indicate that Alagheband and Aref’s signcryption scheme is not secure in their secure model. The analysis also shows that their key management model is not secure. To solve those weaknesses, we also proposed an improved signcryption scheme.

This paper is devoted to investigating the impact of the recurrence of rumors and individual behaviors and control strategies related to rumor spreading in online social networks. To do this, a novel susceptible-hesitating-infected-latent-recovered (SHILR) rumor propagation model in heterogeneous networks is presented. Firstly, based on the relevant mean-field equations of the model, the threshold value is examined to demonstrate the existence and stability of rumor-free/spreading equilibrium with the help of the algebraic method. Secondly, the global stabilities of the equilibria are analyzed through applying Lyapunov stability theory and LaSalle’s invariance principle. Next, the optimal control is proposed by taking advantage of Pontryagin’s maximum principle for reducing the number of infected individuals with minimum cost. Moreover, some numerical examples are carried out to test the theoretical results. Finally, combined with practice, a model application is presented.

The paper presents an approach to mining heterogeneous information networks by decomposing them into homogeneous networks. The proposed HINMINE methodology is based on previous work that classifies nodes in a heterogeneous network in two steps. In the first step the heterogeneous network is decomposed into one or more homogeneous networks using different connecting nodes. We improve this step by using new methods inspired by weighting of bag-of-words vectors mostly used in information retrieval. The methods assign larger weights to nodes which are more informative and characteristic for a specific class of nodes. In the second step, the resulting homogeneous networks are used to classify data either by network propositionalization or label propagation. We propose an adaptation of the label propagation algorithm to handle imbalanced data and test several classification algorithms in propositionalization. The new methodology is tested on three data sets with different properties. For each data set, we perform a series of experiments and compare different heuristics used in the first step of the methodology. We also use different classifiers which can be used in the second step of the methodology when performing network propositionalization. Our results show that HINMINE, using different network decomposition methods, can significantly improve the performance of the resulting classifiers, and also that using a modified label propagation algorithm is beneficial when the data set is imbalanced.

Optimal control of a rumor propagation model in online social network by considering influential nodes

HANDOM: Heterogeneous Attention Network Model for Malicious Domain Detection

The simultaneous use of two or more drugs due to multi-disease comorbidity continues to increase, which may cause adverse reactions between drugs that seriously threaten public health. Therefore, the prediction of drug-drug interaction (DDI) has become a hot topic not only in clinics but also in bioinformatics. In this study, we propose a novel pre-trained heterogeneous graph neural network (HGNN) model named HetDDI, which aggregates the structural information in drug molecule graphs and rich semantic information in biomedical knowledge graph to predict DDIs. In HetDDI, we first initialize the parameters of the model with different pre-training methods. Then we apply the pre-trained HGNN to learn the feature representation of drugs from multi-source heterogeneous information, which can more effectively utilize drugs' internal structure and abundant external biomedical knowledge, thus leading to better DDI prediction. We evaluate our model on three DDI prediction tasks (binary-class, multi-class and multi-label) with three datasets and further assess its performance on three scenarios (S1, S2 and S3). The results show that the accuracy of HetDDI can achieve 98.82% in the binary-class task, 98.13% in the multi-class task and 96.66% in the multi-label one on S1, which outperforms the state-of-the-art methods by at least 2%. On S2 and S3, our method also achieves exciting performance. Furthermore, the case studies confirm that our model performs well in predicting unknown DDIs. Source codes are available at https://github.com/LinsLab/HetDDI.

HAMS: Scalable peer-to-peer multimedia streaming model in heterogeneous networks

This paper focuses on the modeling of a rumor spreading in heterogeneous networks. Using the probability generating function method and pair approximation method, the current research obtains nonlinear differential equations to describe the dynamics of rumor spreading. The comparison between numerical simulations and Monte Carlo simulations confirms the accuracy of our model. Furthermore, the threshold condition is also obtained in this paper. The numerical simulation results show that the heterogeneity of the network accelerates the outbreak of rumors but reduces the maximum density of spreader and the scale of rumors. The present study also examines the effects of parameters on rumor transmission and the differences between rumor transmission recovery mechanisms and disease transmission recovery mechanisms.

Accurate user representation learning has been proven fundamental for many social media applications, including community detection, recommendation, etc. A major challenge lies in that, the available data in a single social network are usually very limited and sparse. In real life, many people are members of several social networks in the same time. Constrained by the features and design of each, any single social platform offers only a partial view of a user from a particular perspective. In this paper, we propose MV-URL, a multi-view user representation learning model to enhance user modeling by integrating the knowledge from various networks. Different from the traditional network embedding frameworks where either the whole framework is single-network based or each network involved is a homogeneous network, we focus on multiple social networks and each network in our task is a heterogeneous network. It's very challenging to effectively fuse knowledge in this setting as the fusion depends upon not only the varying relatedness of information sources, but also the target application tasks. MV-URL focuses on two tasks: user account linkage (i.e., to predict the missing true user account linkage across social media) and user attribute prediction. Extensive evaluations have been conducted on two real-world collections of linked social networks, and the experimental results show the superiority of MV-URL compared with existing state-of-art embedding methods. It can be learned online, and is trivially parallelizable. These qualities make it suitable for real world applications.

Software effort can be measured by story point [35]. Current approaches for automatically estimating story points focus on applying pre-trained embedding models and deep learning for text regression to solve this problem which required expensive embedding models. We propose HeteroSP, a tool for estimating story points from textual input of Agile software project issues. We select GPT2SP [12] and Deep-SE [8] as the baselines for comparison. First, from the analysis of the story point dataset [8], we conclude that software issues are actually a mixture of natural language sentences with quoted code snippets and have problems related to large-size vocabulary. Second, we provide a module to normalize the input text including words and code tokens of the software issues. Third, we design an algorithm to convert an input software issue to a graph with different types of nodes and edges. Fourth, we construct a heterogeneous graph neural networks model with the support of fastText [6] for constructing initial node embedding to learn and predict the story points of new issues. We did the comparison over three scenarios of estimation, including within project, cross-project within the repository, and cross-project cross repository with our baseline approaches. We achieve the average Mean Absolute Error (MAE) as 2.38, 2.61, and 2.63 for three scenarios. We outperform GPT2SP in 2/3 of the scenarios while outperforming Deep-SE in the most challenging scenario with significantly less amount of running time. We also compare our approaches with different homogeneous graph neural network models and the results show that the heterogeneous graph neural networks model outperforms the homogeneous models in story point estimation. For time performance, we achieve about 570 seconds as the time performance in both three processes: node embedding initialization, model construction, and story point estimation.

Numerical simulation of a fractured-vuggy porous medium is a challenging problem. One reason is the coexistence of matrix, fractures and vugs on multiple scales that need to be coupled, and the other reason is that the high-resolution fractured-vuggy model may contain up to several millions of gridcells in applications, which brings severe computational challenges into the numerical methods. Therefore, the requirement for accurate and efficient technique is widely increasing. Fractured-vuggy porous medium is generally represented by triple-continuum model in which the matrix system, fracture system and vug system each are treated as a parallel continuous system. Although triple-continuum model is widely used because of its easy-implementation and high efficiency, it fails to capture the detailed flow patterns of reservoir with disconnected long fractures. Discrete fracture-vug network (DFVN) model can precisely model the fluid flow in fractures and vugs. However, the simulation of this model is deemed intractable even with the advent of supercomputers because of the large amount of calculation. In view of the fact that the multigrid method is now well known as one of the fastest method of solving elliptic problems, in this paper we introduce a nearly linear complexity multiresolution decomposition method for fluid flow in a fractured-vuggy reservoir. The detailed flow patterns are described by combing the advantages of continuum model and discrete model. That is, the homogenization theory is used to construct an equivalent permeability in each coarse grid block in which the vugs and small-scale fractures are represented by discrete fracture-vug network model. We decompose the solution space into several subspaces and then we compute the corresponding solutions of heterogeneous discrete fracture network model in each subspace. Gamblets are constructed and they are elementary solutions of hierarchical information games associated with the process of computing with partial information and limited resources. These gamblets have a natural Bayesian interpretation under the mixed strategy emerging from the game theoretic formulation. This method could realize its fast simulation by decomposing the solution space into a direct sum of linear subspaces that are orthogonal to each other. Finally, the pressure difference distribution of fractured-vuggy porous medium is obtained by combing the DFVN solutions of all subspaces. Numerical results are presented to demonstrate the accuracy and efficiency of the proposed multiresolution decomposition method. The results show that this method is a promising method of numerically simulating the fractured-vuggy porous medium.

In virtual power plants (VPPs), distributed devices (such as residential appliances, energy storage, and electric vehicles) communicate with control centers via wireless access points (APs). Nevertheless, with a massive number of devices connected to APs, VPPs would be adversely affected by packet loss and consequently suffer from revenue reduction. Therefore, devices’ access connections need to be delicately designated to mitigate these negative impacts. This paper studies the device access optimization problem from the perspective of VPP operations. An optimization model to minimize the revenue reduction induced by packet loss is first formulated to designate APs for devices in heterogeneous communication networks. The significance of various device data is determined by revenue reduction as a result of packet loss. Additionally, the heterogeneous access network model is established to characterize different types of APs. To make the model implementable, this paper further proposes a layered decomposition solution algorithm, which decomposes optimization tasks into small-scale sub-problems and successively assigns them to different layers of APs. Sub-problems are further decomposed for APs to greedily cut revenue reduction by evaluating devices’ marginal contributions to revenue reduction. Numerical case studies are also conducted to verify the benefits of device access optimization.