Heterogeneous Network Model Research Articles

On-off attack detection in trust model using intra-daily variability for the IoT

The growth of the internet of things (IoT) increases the need to develop the trust computational model for heterogeneous networks with various IoT devices. Trust models are considered as an effective tool to mitigate insider attacks induced by IoT devices. However, trust models are exposed to on-off attacks, in which devices randomly exhibit good and bad behaviors to avoid being categorized as low-trust devices. The objective of this work is to recognize the malicious devices executing on-off attacks in IoT applications. This paper introduces an on-off attack detection strategy for the trust computational model based on the non-parametric index named intra-daily variability (IV). IV indicates trust fragmentation which depends on the frequency and the transitions between periods of low and high trust values of a device. The higher value of IV indicates the occurrence of fragmented trust values and the lower value of IV indicates the occurrence of non-fragmented trust values. Experimental results show that the proposed model outperforms the baseline methods by increasing the on-off attack detection rate.

Conformational transitions of the HIV-1 Gag polyprotein upon multimerization and gRNA binding

During the HIV-1 assembly process, the Gag polyprotein multimerizes at the producer cell plasma membrane, resulting in the formation of spherical immature virus particles. Gag-genomic RNA (gRNA) interactions play a crucial role in the multimerization process, which is yet to be fully understood. We performed large-scale all-atom molecular dynamics simulations of membrane-bound full-length Gag dimer, hexamer, and 18-mer. The inter-domain dynamic correlation of Gag, quantified by the heterogeneous elastic network model applied to the simulated trajectories, is observed to be altered by implicit gRNA binding, as well as the multimerization state of the Gag. The lateral dynamics of our simulated membrane-bound Gag proteins, with and without gRNA binding, agree with prior experimental data and help to validate our simulation models and methods. The gRNA binding is observed to affect mainly the SP1 domain of the 18-mer and the matrix-capsid linker domain of the hexamer. In the absence of gRNA binding, the independent dynamical motion of the nucleocapsid domain results in a collapsed state of the dimeric Gag. Unlike stable SP1 helices in the six-helix bundle, without IP6 binding, the SP1 domain undergoes a spontaneous helix-to-coil transition in the dimeric Gag. Together, our findings reveal conformational switches of Gag at different stages of the multimerization process and predict that the gRNA binding reinforces an efficient binding surface of Gag for multimerization, and also regulates the dynamic organization of the local membrane region itself.

An adaptive optimized handover decision model for heterogeneous networks

A heterogeneous network (HetNet), combining different technologies, is considered a promising solution adopted by several upcoming generations of mobile networks to keep up with the rapid development of mobile users’ requirements while improving network performance. In this scenario, a vertical handover (VHO) algorithm is responsible for ensuring the continuity of the ongoing user connection while moving within the coverage of the HetNet. Although various VHO algorithms were proposed, achieving efficient performance from both network and user perspectives remains challenging. This paper proposes an adaptive optimized vertical handover algorithm based on a multi-attribute decision-making (MADM) algorithm integrated with particle swarm optimization and gravitational search algorithm (PSOGSA) as a framework to implement the handover process. The algorithm includes three main ideas. Firstly, a network selection framework is proposed considering the most important criteria, including signal strength and other networks’ attributes, along with users’ characteristics regarding their mobility and service preferences. Secondly, two new parameters are introduced as control handover parameters named load factor (LF) and score priority (SP) to reduce unnecessary handovers and the overall HetNet power consumption while achieving balanced load distribution. Lastly, the desired aims are formulated as an objective function, then the PSOGSA algorithm is used to reach the optimal values of both LF and SP, which will be considered when executing the handover algorithm. The presented algorithm is simulated in a heterogeneous wireless network where the fifth-generation (5G) wireless technology coexists with other radio access networks to improve the evaluation field of the proposed algorithm. Also, the proposed algorithm’s performance is evaluated in the case of using various MADM algorithms. The simulation results show that the proposed adaptive optimized approach attains efficient performance by decreasing unnecessary handovers by more than 40% and achieving much better load distribution by around 20% to 40%, outperforming traditional handover approaches.

Prediction of Covid-19 Multiparametric Biomarkers and Drug Target of Patients for Risk Stratification Using Machine Learning Approach

In the situation of Coronavirus disease 2019 (COVID-19), forecasting disease progression and identifying therapeutic drug targets is critical, especially given the nonattendance of a viable approach for treating severe cases. The preparation cohort revealed promising biomarkers, which were then precisely measured and employed to assess prediction accuracy across validation cohorts. This approach holds significant potential in enhancing understanding of severe COVID-19 and aiding the development of effective treatments. However, ultrasound-guided MRI (US-MRI) is an emerging modality that can noninvasively acquire multi-parametric information on COVID-19 and function without the need for contrast agents. This shows that neural network analysis of US-MRI transports exclusive prognosis data and this significantly improved prognosis performance. Consequently, the research proposed a deep neural network model of an Ensemble Multi-Relational Graph Neural Network (EMR-GNN) to determine the optimal model for predicting vascular biomarkers (CRP, IL-6, ferritin). In the nonappearance of a tailored treatment for this emerging virus, scientists are actively investigating various strategies to curb its replication. This work focuses on identifying potential drug targets, drawing from proteins abundant in lung material and those targeted by FDA-approved drugs as catalogued in HPA. This effort reflects a broader initiative within the methodical unrestricted to develop effective means of limiting virus replication. Accordingly, recognized five lung-improved proteins, comprising MRC1, SG3A1, CCL18, histone H4, and CLEC3B, were annotated as “drug targets”. For this, the researcher proposes a Heterogeneous Graph Structural Attention Neural Network (HGS-ANN) model to learn topological information of composite molecules and a Dilated Causal CNN-LSTM model with U-Net layers for modelling spatial-sequential information in Simplified Molecular-Input Line-Entry System (SMILES) sequences of drug data. The COVID-19 datasets are downloaded from the GEO database. These data are evaluated using Matlab software. The proposed work evaluated that the AUC of the work is 0.995, however, the AUC is measured based on sex, age, and chronic diseases. This model has a 0.933 accuracy in the subgroup of slices thicker than 1mm. However, the AUC curve and the classification outcome of the proposed method are compared with the existing rad model, deeper, and KNN models. In comparison to existing methods, the proposed model demonstrates superior performance. This research not only identifies potential therapeutic targets nonetheless also serves to uncover biomarkers crucial for comprehending the pathogenesis of undecorated COVID-19.

SHGCN: Socially Enhanced Heterogeneous Graph Convolutional Network for Multi-behavior Prediction

In recent years, multi-behavior information has been utilized to address data sparsity and cold-start issues. The general multi-behavior models capture multiple behaviors of users to make the representation of relevant features more fine-grained and informative. However, most current multi-behavior recommendation methods neglect the exploration of social relations between users. Actually, users’ potential social connections are critical to assist them in filtering multifarious messages, which may be one key for models to tap deeper into users’ interests. Additionally, existing models usually focus on the positive behaviors (e.g., click , follow , and purchase ) of users and tend to ignore the value of negative behaviors (e.g., unfollow and badpost ). In this work, we present a Multi-Behavior Graph (MBG) construction method based on user behaviors and social relationships and then introduce a novel socially enhanced and behavior-aware graph neural network for behavior prediction. Specifically, we propose a Socially Enhanced Heterogeneous Graph Convolutional Network (SHGCN) model, which utilizes behavior heterogeneous graph convolution module and social graph convolution module to effectively incorporate behavior features and social information to achieve precise multi-behavior prediction. In addition, the aggregation pooling mechanism is suggested to integrate the outputs of different graph convolution layers, and a dynamic adaptive loss (DAL) method is presented to explore the weight of each behavior. The experimental results on the datasets of the e-commerce platforms (i.e., Epinions and Ciao) indicate the promising performance of SHGCN. Compared with the most powerful baseline, SHGCN achieves 3.3% and 1.4% uplift in terms of AUC on the Epinions and Ciao datasets. Further experiments, including model efficiency analysis, DAL mechanism, and ablation experiments, confirm the validity of the multi-behavior information and social enhancement.

OpenMSCG: A Software Tool for Bottom-Up Coarse-Graining.

The "bottom-up" approach to coarse-graining, for building accurate and efficient computational models to simulate large-scale and complex phenomena and processes, is an important approach in computational chemistry, biophysics, and materials science. As one example, the Multiscale Coarse-Graining (MS-CG) approach to developing CG models can be rigorously derived using statistical mechanics applied to fine-grained, i.e., all-atom simulation data for a given system. Under a number of circumstances, a systematic procedure, such as MS-CG modeling, is particularly valuable. Here, we present the development of the OpenMSCG software, a modularized open-source software that provides a collection of successful and widely applied bottom-up CG methods, including Boltzmann Inversion (BI), Force-Matching (FM), Ultra-Coarse-Graining (UCG), Relative Entropy Minimization (REM), Essential Dynamics Coarse-Graining (EDCG), and Heterogeneous Elastic Network Modeling (HeteroENM). OpenMSCG is a high-performance and comprehensive toolset that can be used to derive CG models from large-scale fine-grained simulation data in file formats from common molecular dynamics (MD) software packages, such as GROMACS, LAMMPS, and NAMD. OpenMSCG is modularized in the Python programming framework, which allows users to create and customize modeling "recipes" for reproducible results, thus greatly improving the reliability, reproducibility, and sharing of bottom-up CG models and their applications.

MSHGANMDA: Meta-Subgraphs Heterogeneous Graph Attention Network for miRNA-Disease Association Prediction.

MicroRNAs (miRNAs) influence several biological processes involved in human disease. Biological experiments for verifying the association between miRNA and disease are always costly in terms of both money and time. Although numerous biological experiments have identified multi-types of associations between miRNAs and diseases, existing computational methods are unable to sufficiently mine the knowledge in these associations to predict unknown associations. In this study, we innovatively propose a heterogeneous graph attention network model based on meta-subgraphs (MSHGANMDA) to predict the potential miRNA-disease associations. Firstly, we define five types of meta-subgraph from the known miRNA-disease associations. Then, we use meta-subgraph attention and meta-subgraph semantic attention to extract features of miRNA-disease pairs within and between these five meta-subgraphs, respectively. Finally, we apply a fully-connected layer (FCL) to predict the scores of unknown miRNA-disease associations and cross-entropy loss to train our model end-to-end. To evaluate the effectiveness of MSHGANMDA, we apply five-fold cross-validation to calculate the mean values of evaluation metrics Accuracy, Precision, Recall, and F1-score as 0.8595, 0.8601, 0.8596, and 0.8595, respectively. Experiments show that our model, which primarily utilizes multi-types of miRNA-disease association data, gets the greatest ROC-AUC value of 0.934 when compared to other state-of-the-art approaches. Furthermore, through case studies, we further confirm the effectiveness of MSHGANMDA in predicting unknown diseases.

A heterogeneous graph convolutional attention network method for classification of autism spectrum disorder

BackgroundAutism spectrum disorder (ASD) is a serious developmental disorder of the brain. Recently, various deep learning methods based on functional magnetic resonance imaging (fMRI) data have been developed for the classification of ASD. Among them, graph neural networks, which generalize deep neural network models to graph structured data, have shown great advantages. However, in graph neural methods, because the graphs constructed are homogeneous, the phenotype information of the subjects cannot be fully utilized. This affects the improvement of the classification performance.MethodsTo fully utilize the phenotype information, this paper proposes a heterogeneous graph convolutional attention network (HCAN) model to classify ASD. By combining an attention mechanism and a heterogeneous graph convolutional network, important aggregated features can be extracted in the HCAN. The model consists of a multilayer HCAN feature extractor and a multilayer perceptron (MLP) classifier. First, a heterogeneous population graph was constructed based on the fMRI and phenotypic data. Then, a multilayer HCAN is used to mine graph-based features from the heterogeneous graph. Finally, the extracted features are fed into an MLP for the final classification.ResultsThe proposed method is assessed on the autism brain imaging data exchange (ABIDE) repository. In total, 871 subjects in the ABIDE I dataset are used for the classification task. The best classification accuracy of 82.9% is achieved. Compared to the other methods using exactly the same subjects in the literature, the proposed method achieves superior performance to the best reported result.ConclusionsThe proposed method can effectively integrate heterogeneous graph convolutional networks with a semantic attention mechanism so that the phenotype features of the subjects can be fully utilized. Moreover, it shows great potential in the diagnosis of brain functional disorders with fMRI data.

A product requirement development method based on multi-layer heterogeneous networks

The rapid advancement of emerging technologies in the information age has resulted in intricate, diverse, and rapidly changing user requirements, thereby reducing the effectiveness of traditional requirement development approaches. This paper proposes a multi-layer heterogeneous network-based method for product requirement development to identify key requirements in the product development process and address the complex relationships among users, requirements, products, and requirement development teams. By incorporating the theory of hesitant fuzzy into the expert decision-making process, this method constructs four single-layer networks: user attention network, expert trust network, requirement correlation network, and component combination network, which assigns realistic significance to nodes in each network layer and establishes connections between different networks, forming a multi-layer heterogeneous network model for product requirement development. Furthermore, this paper defines indicators such as layer significance and node comprehensive significance and proposes a calculation method for node comprehensive significance based on biased random walk, which enables deep integration of node’s realistic significance and topological centrality. The proposed method is applied to a data experiment of product requirement development for a specific type of new energy vehicle. By analyzing the calculation results of node comprehensive significance, high-quality recommendations are provided for the development team from the perspectives of functional requirements, product components, and user groups. Moreover, comparative experiments to assess algorithm performance demonstrate that the average accuracy of the proposed algorithm is 0.95, which is significantly higher than that of the commonly used single-layer network node centrality measurement methods and recently proposed multi-layer network node centrality measurement methods. These results reflect the advancement and applicability of the proposed method in product development scenarios.

An improved heterogeneous graph convolutional network for job recommendation

Job recommendation is crucial in online recruitment platforms due to the overwhelming number of job postings. Job seekers spend considerable time and effort searching for suitable employment. With millions of job seekers browsing job postings daily, the demand for accurate and effective job recommendations is more pressing than ever. To address this challenge, we propose IHGCN, an improved semi-supervised heterogeneous graph convolutional network model for job recommendation. IHGCN aims to provide job recommendations for early job seekers based on their resumes. Firstly, we introduce a novel labeling classification standard specifically tailored to early job seeker resumes. Secondly, we construct a heterogeneous resume graph where each resume is represented as a node. Job recommendation is treated as a multi-classification problem. Thirdly, our IHGCN model learns a node representation from the graph to perform effective job recommendations. To evaluate our model, we conduct experiments using a real-world resume dataset obtained from LinkedIn. The results demonstrate that IHGCN outperforms the baselines by around 10%. This study highlights the benefits of leveraging meta-paths within the Graph Convolutional Network model to address the sparsity problem caused by the one-hot representation of nodes.

The spread dynamics model of the interaction between rumors and derivative rumors in emergencies under the control strategy

Emergencies are usually accompanied by rumors, which are intertwined with each other and cause interactive effects, leading to crisis escalation and “secondary disasters”. How to control and weaken such an effect is a very important topic in the field of spread dynamics. In this paper, we consider the interaction between rumors and derivative rumors, introduce the IC control strategy, construct the SIDR-SIR model of a two-stage multistate transfer based on heterogeneous networks, and set up the transfer process of 9 states in the first stage and 12 states in the second stage of the model. Furthermore, the two-stage mean field equation of the model in heterogeneous networks is given, and the critical conditions of the spread are derived. Finally, the effects of the interaction and the control strategies between two rumors spreading are studied by numerical simulation. The results show that rumors with the mutual promotion will have a wider propagation scale under the impact of the interaction, and the control of one rumor, either by the earlier timing of the introduction of the control strategy or by the greater control strength, will play an inhibitory role in the spread of the other rumor. Different control measures have different effects on rumor governance. Increasing the proportion of the government isolating the initial rumor and converting the public into rumor debunkers for initial rumors and increasing government clarification for derivative rumors will both have better governance effects. This work provides a reference for the government of rumors and derivative rumors in emergencies.

HetDDI: a pre-trained heterogeneous graph neural network model for drug-drug interaction prediction.

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.

Dual-stage ensemble approach using online knowledge distillation for forecasting carbon emissions in the electric power industry

The electric power industry is the key to achieving the goals of carbon peak and neutrality. Accurate forecasting of carbon emissions in the electric power industry can aid in the prompt adjustment of power generation policies and the early achievement of carbon reduction targets. This study proposes a new approach that combines the decomposition-ensemble paradigm with knowledge distillation to forecast daily carbon emissions. First, seasonal and trend decomposition using locally weighted scatterplot smoothing (STL) is used to decompose the data into three subcomponents. Second, two heterogeneous deep neural network models are jointly trained to predict each subcomponent based on online knowledge distillation. During training, the two models learn and provide feedback to each other. The first model-ensemble stage is performed by synthesizing the predictions for each subcomponent of the two models. Finally, the second model-ensemble stage is performed. The predictions for each subcomponent are integrated using linear addition to obtain the final results. In addition, to avoid leakage of test data caused by decomposing the entire time series, a recursive forecasting strategy is applied. Multistep predictions are obtained by forecasting 7, 15, and 30 days in the future. Experimental results using metaheuristic algorithms to optimize hyperparameters show that the proposed method evaluated on the daily carbon emissions dataset has better forecasting performance than all baselines.

Deep Reinforcement Learning-Based Energy Minimization Task Offloading and Resource Allocation for Air Ground Integrated Heterogeneous Networks

With the popularization of Internet of Things, a number of emerging applications all require both efficient communication and computing service, which poses enormous challenges to the computing ability and battery capacity of terminal equipment. Moreover, the ground-based 5G system cannot provide seamless service especially for hotspot and remote area. In order to deal with the mentioned issues, we first propose an air ground integrated heterogeneous networks model, which consists of multiple UAVs and GBSs equipped with edge servers. Then, by jointly taking into account terminal power control, computing resource allocation, and task offloading decision, an energy-minimization issue is formulated, which, however, is a mixed integer nonlinear programming problem due to the tight coupling between optimization variables. Therefore, we decompose it into two subproblems and design a deep actor-critic-based online offloading algorithm to solve the first offloading decision-making issue facing dimensionality curse. For the second power control and computing resource allocation subproblem, a difference-of-convex-based solution is presented. The proposed approach can achieve superior performance in terms of terminal energy consumption and convergence speed with lower complexity compared with other benchmark methods. Specifically, it outperforms DDQN, AC&Greedy, and UCB algorithm by 7.62%, 17.32%, and 23.14%, respectively.

Unveiling hierarchical relationships for social image representation learning

Amidst the rapid propagation of images across social media platforms, the representation learning of social images has emerged as a prime research pursuit. A salient characteristic of social images lies in their multimodal properties, encompassing the visual content and textual descriptions present within individual images and the social relationships among different images. Notably, the content information within individual images and the structural information among different images exhibit a hierarchical nature. However, existing endeavors generally employ a flat framework, leading to suboptimal utilization of hierarchical relationships. Furthermore, the heterogeneity inherent in data content and structural information presents additional challenges in social image representation. In light of these challenges, we propose a novel Hierarchical Heterogeneous Graph Neural Network model for Social Image Representation learning, dubbed HHGSI. Our motivation lies in exploring and exploiting the hierarchical relationship between diverse modalities through designing the hierarchical heterogeneous network framework. HHGSI consists of an Intra-node Multimodal Graph Encoder and an Inter-node Heterogeneous Graph Neural Network to simultaneously capture fine-grained correlation within the image and heterogeneous relationships among the images. Moreover, a task-independent optimization objective is designed to make the model suitable for numerous network-oriented and multimodal tasks. Our proposal is extensively evaluated over four real-world datasets, and experimental results demonstrate the superiority of our proposal. Our code is publicly available in https://github.com/multimodal-code/HHGSI.

HMSG: Heterogeneous graph neural network based on Metapath SubGraph learning

Heterogeneous graph neural network (HGNN) models, capable of learning low-dimensional dense vectors from heterogeneous graphs for downstream graph-mining tasks, have attracted increasing attention in recent years. For these models, metapath-based methods have been widely adopted. However, most existing metapath-based HGNN models either discard intermediate nodes within a metapath, resulting in information loss, or indiscriminately aggregate information along a metapath containing different types of nodes, resulting in unavoidable learning bias. To overcome these limitations, a new HGNN model named HMSG, is proposed in this paper to comprehensively capture structural, semantic and attribute information from both homogeneous and heterogeneous neighbors more purposefully. To achieve this, a type-specific linear transformation is first applied to transfer the node attributes to different types of nodes with the same latent factor space. In the new model, the heterogeneous graph is decomposed into multiple metapath-based homogeneous and heterogeneous subgraphs where each subgraph associates specific semantic and structural information; this is different from existing models, which mainly rely on symmetric metapaths. Subsequently, tailored attention-based message aggregation methods are independently applied to each subgraph such that information learning can be more targeted. Finally, information from different subgraphs is fused through graph-level attention to obtain a complete representation. The learned representations are evaluated by several graph-mining tasks. Results indicate that the HMSG attains the best performance in all evaluation metrics than state-of-the-art baselines. Further ablation experiments demonstrate the effectiveness of the modules designed for the HMSG.

TenGAN: adversarially generating multiplex tensor graphs

In this work, we explore multiplex graph (networks with different types of edges) generation with deep generative models. We discuss some of the challenges associated with multiplex graph generation that make it a more difficult problem than traditional graph generation. We propose TenGAN, the first neural network for multiplex graph generation, which greatly reduces the number of parameters required for multiplex graph generation. We also propose 3 different criteria for evaluating the quality of generated graphs: a graph-attribute-based, a classifier-based, and a tensor-based method. We evaluate its performance on 4 datasets and show that it generally performs better than other existing statistical multiplex graph generative models. We also adapt HGEN, an existing deep generative model for heterogeneous information networks, to work for multiplex graphs and show that our method generally performs better.

A heterogeneous graph neural network model for list recommendation

List recommendation, an approach for recommending a set of items closely related to user preferences, plays a significant role in improving the user experience and has received growing attention in recent years. However, most existing models ignore list-level attributes and the heterogeneity of the interactions between the users and lists when predicting user preferences. In this paper, we propose a heterogeneous graph neural network list recommendation (HGLR) model to bridge these gaps. First, a representation learning module based on pre-training techniques is developed to learn the embeddings of the lists, by encoding textual information and aggregating the features of the constituent items. We then transform the user–list interaction into a bipartite graph and incorporate the list attributes into a heterogeneous information network (HIN). Propagation-based embedding learning is subsequently performed to generate user and list representation based on the message-passing mechanism of the graph neural network. Finally, an attentive fusion layer is designed to obtain the global representations for the recommendation. We also develop a new large-scale dataset containing multiple types of side information to evaluate the list recommendation model. Extensive experiments demonstrate that our model significantly outperforms state-of-the-art list recommendation methods and achieves performance gains over 8.96% and 14.59% in Recall and NDCG, respectively, against the strongest baselines.

ClueReader: Heterogeneous Graph Attention Network for Multi-Hop Machine Reading Comprehension

Multi-hop machine reading comprehension is a challenging task in natural language processing as it requires more reasoning ability across multiple documents. Spectral models based on graph convolutional networks have shown good inferring abilities and lead to competitive results. However, the analysis and reasoning of some are inconsistent with those of humans. Inspired by the concept of grandmother cells in cognitive neuroscience, we propose a heterogeneous graph attention network model named ClueReader to imitate the grandmother cell concept. The model is designed to assemble the semantic features in multi-level representations and automatically concentrate or alleviate information for reasoning through the attention mechanism. The name ClueReader is a metaphor for the pattern of the model: it regards the subjects of queries as the starting points of clues, takes the reasoning entities as bridge points, considers the latent candidate entities as grandmother cells, and the clues end up in candidate entities. The proposed model enables the visualization of the reasoning graph, making it possible to analyze the importance of edges connecting entities and the selectivity in the mention and candidate nodes, which is easier to comprehend empirically. Evaluations on the open-domain multi-hop reading dataset WikiHop and drug–drug interaction dataset MedHop proved the validity of ClueReader and showed the feasibility of its application of the model in the molecular biology domain.

Robustness of Double-Layer Group-Dependent Combat Network with Cascading Failure

The networked combat system-of-system (CSOS) is the trend of combat development with the innovation of technology. To achieve the combat effectiveness, studying the ability of CSOS to cope with external interference is of great importance. Here we report a modeling method of CSOS from the perspective of complex networks and explore the robustness of the combat network based on this. Firstly, a more realistic double-layer heterogeneous dependent combat network model is established. Then, the conditional group dependency situation is considered to design failure rules for dependent failure, and the coupling relation between the double-layer subnets is analyzed for overload failure. Based on this, the initial load and capacity of the node are defined, respectively, as well as the load redistribution strategy and the status judgment rules for the cascading failure model. Simulation experiments are carried out by changing the attack modes and different parameters, and the results show that the robustness of the combat network can be effectively improved by improving the tolerance limit of one-way dependency of the functional net, the node capacity of the functional subnet, and the tolerance of the overload state. The conclusions of this paper can provide a useful reference for network structure optimization and network security protection in the military field.