Multidimensional Network Research Articles

Frequent Itemset Mining and Multi-Layer Network-Based Analysis of RDF Databases

Triplestores or resource description framework (RDF) stores are purpose-built databases used to organise, store and share data with context. Knowledge extraction from a large amount of interconnected data requires effective tools and methods to address the complexity and the underlying structure of semantic information. We propose a method that generates an interpretable multilayered network from an RDF database. The method utilises frequent itemset mining (FIM) of the subjects, predicates and the objects of the RDF data, and automatically extracts informative subsets of the database for the analysis. The results are used to form layers in an analysable multidimensional network. The methodology enables a consistent, transparent, multi-aspect-oriented knowledge extraction from the linked dataset. To demonstrate the usability and effectiveness of the methodology, we analyse how the science of sustainability and climate change are structured using the Microsoft Academic Knowledge Graph. In the case study, the FIM forms networks of disciplines to reveal the significant interdisciplinary science communities in sustainability and climate change. The constructed multilayer network then enables an analysis of the significant disciplines and interdisciplinary scientific areas. To demonstrate the proposed knowledge extraction process, we search for interdisciplinary science communities and then measure and rank their multidisciplinary effects. The analysis identifies discipline similarities, pinpointing the similarity between atmospheric science and meteorology as well as between geomorphology and oceanography. The results confirm that frequent itemset mining provides an informative sampled subsets of RDF databases which can be simultaneously analysed as layers of a multilayer network.

Community Found: Young Adult Catholic Identity and Commitment

AbstractThis article attempts to fill the gap in the sociological literature at the intersection of religion and community by examining how local, urban Catholic churches shape and nurture community commitment. My empirical research shows that the collective identity of young adult Catholics emerges from patterns of religious identity that are activated in church settings and defined through meaningful role identities established by the organizational framing and resonance of clergy leadership. As previous research has shown, participation in church activities and strong social connections within congregations contribute to greater voluntarism and commitment to the larger community. Further, collective efforts achieve greater success if the stated goals relate back to shared collective identities and an aspirational source of shared moral obligation that gives weight to the voluntarist commitment. The example of local Catholic churches highlights the importance of multidimensional social networks that can align collective identities with bespoke calls to action amid the evolving needs and aspirations of multiple groups and constituencies.

Combining Multi-Dimensional Convolutional Neural Network (CNN) With Visualization Method for Detection of Aphis gossypii Glover Infection in Cotton Leaves Using Hyperspectral Imaging.

Cotton is a significant economic crop. It is vulnerable to aphids (Aphis gossypii Glovers) during the growth period. Rapid and early detection has become an important means to deal with aphids in cotton. In this study, the visible/near-infrared (Vis/NIR) hyperspectral imaging system (376–1044 nm) and machine learning methods were used to identify aphid infection in cotton leaves. Both tall and short cotton plants (Lumianyan 24) were inoculated with aphids, and the corresponding plants without aphids were used as control. The hyperspectral images (HSIs) were acquired five times at an interval of 5 days. The healthy and infected leaves were used to establish the datasets, with each leaf as a sample. The spectra and RGB images of each cotton leaf were extracted from the hyperspectral images for one-dimensional (1D) and two-dimensional (2D) analysis. The hyperspectral images of each leaf were used for three-dimensional (3D) analysis. Convolutional Neural Networks (CNNs) were used for identification and compared with conventional machine learning methods. For the extracted spectra, 1D CNN had a fine classification performance, and the classification accuracy could reach 98%. For RGB images, 2D CNN had a better classification performance. For HSIs, 3D CNN performed moderately and performed better than 2D CNN. On the whole, CNN performed relatively better than conventional machine learning methods. In the process of 1D, 2D, and 3D CNN visualization, the important wavelength ranges were analyzed in 1D and 3D CNN visualization, and the importance of wavelength ranges and spatial regions were analyzed in 2D and 3D CNN visualization. The overall results in this study illustrated the feasibility of using hyperspectral imaging combined with multi-dimensional CNN to detect aphid infection in cotton leaves, providing a new alternative for pest infection detection in plants.

Multi-dimensional proximity and network stability: the moderating role of network cohesion

Previous studies regarded network stability as an antecedent of innovation. However, there is little research on its antecedent factors. The paper explores and extends the prior research to analyze the influence of network cohesion on network stability within the framework of multi-dimensional proximity. We collected the unbalanced panel data in the information communication technology industry based on the Derwent Innovations Index databases. The research results show that geographic proximity and technological proximity have an invert-U relationship with network stability, and institutional proximity has positively related to network stability. Moreover, network cohesion positively moderates the impact of multi-dimensional proximity on network stability, and network cohesion has a significant effect on network stability.

Multidimensional Intelligent Distribution Network Load Analysis and Forecasting Management System Based on Multidata Fusion Technology

In order to improve the work efficiency of load characteristic analysis and realize lean management, scientific prediction, and reasonable planning of the distribution networks, this paper develops a multidimensional intelligent distribution network load analysis and prediction management system based on the fusion of multidimensional data for the application of multidimensional big data in the smart distribution network. First, the framework of the software system is designed, and the functional modules for multidimensional load characteristic analysis are designed. Then, the method of multidimensional user load characterization is introduced; furthermore, the application functions and the design process of some important function modules of the software system are introduced. Finally, an application example of the multidimensional user load characterization system is presented. Overall, the developed system has the features of interoperability of data links between functional modules, information support between different functions, and modular design concept, which can meet the daily application requirements of power grid enterprises and can respond quickly to the issued calculation requirements.

HeteroGraphRec: A heterogeneous graph-based neural networks for social recommendations

Recommender systems in social networks are widely used for connecting users to their desired items from a vast catalog of available items. Learning the user’s preferences from all the possible sources of information in an extensive, multi-dimensional social network is one of the main challenges when building such recommenders. Graph Neural Networks have been gaining momentum in recent years and have been successful when dealing with large-scale graphs, and they can be applied to social networks with some modifications. In this research, we propose the HeteroGraphRec, which provides social recommendations by modeling the social network as a heterogeneous graph and utilizing GNNs with attention mechanisms to intelligently aggregate information from all sources when building the connections between user to user, item to item, and user to item. The HeteroGraphRec can gather information about the user’s connections (friendships, trust network), item interaction history, and item similarities to attain rich information about the preferences. To evaluate the HeteroGraphRec, we use three real-world benchmark datasets and demonstrate that the proposed HeteroGraphRec achieves superior performance compared to ten other state-of-the-art social recommender systems. We extensively analyze the HeteroGraphRec model to illustrate the effectiveness by changing the embedding dimensions of the users and items. We also show the interpretability of our model by examining each component of the model’s contribution. The results show that the HeteroGraphRec is robust and can consistently perform better than the baseline systems.

Predicting and Interpreting Spatial Accidents through MDLSTM.

Predicting and interpreting the spatial location and causes of traffic accidents is one of the current hot topics in traffic safety. This research purposed a multi-dimensional long-short term memory neural network model (MDLSTM) to fit the non-linear relationships between traffic accident characteristics and land use properties, which are further interpreted to form local and general rules. More variables are taken into account as the input land use properties and the output traffic accident characteristics. Five types of traffic accident characteristics are simultaneously predicted with higher accuracy, and three levels of interpretation, including the hidden factor-traffic potential, the potential-determine factors, which varies between grid cells, and the general rules across the whole study area are analyzed. Based on the model, some interesting insights were revealed including the division line in the potential traffic accidents in Shenyang (China). It is also purposed that the relationship between land use and accidents differ from previous researches in the neighboring and regional aspects. Neighboring grids have strong spatial connections so that the relationship of accidents in a continuous area is relatively similar. In a larger region, the spatial location is found to have a great influence on the traffic accident and has a strong directionality.

An Intrusion Detection Model Based on SMOTE and Convolutional Neural Network Ensemble

Massive, multi-dimensional and imbalanced network traffic data has brought new challenges to traditional intrusion detection systems (IDSs). The detection performance of traditional algorithms is closely related to feature extractions, which are not effective in the massive and imbalanced data environments. In this paper, we propose an intrusion detection model based on synthetic minority oversampling technology (SMOTE) and convolutional neural network (CNN) ensemble. It converts original traffic vectors into images, designs a CNN structure, and combines SMOTE and CNN ensemble to solve the problem of imbalanced datasets. Using the standard KDD CUP 99 dataset to evaluate the performance of the proposed model and analysing the contribution of features to model decision-making show that the model’s F1 score are better than traditional algorithms in the classes with few samples and the model improves the efficiency of network intrusion detection.

Metabolomics-based multidimensional network biomarkers for diabetic retinopathy identification in patients with type 2 diabetes mellitus

IntroductionDespite advances in diabetic retinopathy (DR) medications, early identification is vitally important for DR administration and remains a major challenge. This study aims to develop a novel system of multidimensional...

Investigation on the Efficiency of Chinese Herbal Injections for Treating Non-small Cell Lung Cancer With Vinorelbine and Cisplatin Based on Multidimensional Bayesian Network Meta-Analysis.

Background: As non-small cell lung cancer (NSCLC) seriously threatens human health, several clinical studies have reported that Chinese herbal injections (CHIs) combined with vinorelbine and cisplatin (NP) are beneficial. This multidimensional network meta-analysis was performed to explore the preferable options among different CHIs for treating NSCLC. Methods: A literature search was performed in several databases to identify randomized controlled trials (RCTs) of CHIs in the treatment of NSCLC from inception to January 31, 2019. Final included studies met the eligibility criteria and methodological quality recommendations. Data analysis was performed using Stata 13.0 and WinBUGS 14.0 software. Each outcome was presented as an odds ratio and the surface under the cumulative ranking curve value (SCURA). The “scatterplot3d” package in R 3.6.1 software was used to perform multidimensional cluster analysis. Results: Ultimately, 97 eligible RCTs involving 7,440 patients and 14 CHIs were included in this network meta-analysis. Combined with NP chemotherapy, Kanglaite injection plus NP exhibited a better impact on the clinical effectiveness rate (SCURA probability: 78.34%), and Javanica oil emulsion injection plus NP was better in the performance status (95.44%). Huachansu injection plus NP was dominant in reducing thrombocytopenia (92.67%) and gastrointestinal reactions (92.52%). As to multidimensional cluster analysis, Shenmai injection plus NP was superior considering improving the clinical effectiveness rate, performance status and relieving leukopenia. Conclusions: The combination of CHIs and NP has a better impact on patients with NSCLC than NP alone. Among them, Shenmai injection plus NP, Kanglaite injection plus NP and Javanica oil emulsion injection plus NP were notable. Nevertheless, more multicenter and better designed RCTs are needed to validate our findings.

Filogenias: conceptos y generalidades

Any research in biology is an exercise of comparison that includes the study of evolution. The investigation of evolutionary patterns in either of its two approaches (micro or macroevolutionary) raises methodological challenges for any researcher interested in these topics. These approaches have a common interest in understanding the origin the relationships between the studied organisms, although the temporal scales and the level of organization in which they focus are different. Currently, phylogenies are the most robust tool to explain ancestor-descendant relationships between a set of organisms. These diagrams, which are two-dimensional (cladograms) or multidimensional (networks), can be estimated with different approximations (maximum parsimony, máximum likelihood, and bayesian inference) according to the data available and the purpose of the investigation. This review presents an introduction to the methods available for the construction of phylogenies, including the traditional perspective that uses diagrams based on dichotomies and the new trends that try to visualize more complex patterns through evolutionary networks.

Bone marrow adipogenic lineage precursors promote osteoclastogenesis in bone remodeling and pathologic bone loss.

Bone is maintained by coupled activities of bone-forming osteoblasts/osteocytes and bone-resorbing osteoclasts. Alterations in this relationship can lead to pathologic bone loss such as osteoporosis. It is well known that osteogenic cells support osteoclastogenesis via production of RANKL. Interestingly, our recently identified bone marrow mesenchymal cell population-marrow adipogenic lineage precursors (MALPs) that form a multidimensional cell network in bone-was computationally demonstrated to be the most interactive with monocyte-macrophage lineage cells through high and specific expression of several osteoclast regulatory factors, including RANKL. Using an adipocyte-specific Adipoq-Cre to label MALPs, we demonstrated that mice with RANKL deficiency in MALPs have a drastic increase in trabecular bone mass in long bones and vertebrae starting from 1 month of age, while their cortical bone appears normal. This phenotype was accompanied by diminished osteoclast number and attenuated bone formation at the trabecular bone surface. Reduced RANKL signaling in calvarial MALPs abolished osteolytic lesions after LPS injections. Furthermore, in ovariectomized mice, elevated bone resorption was partially attenuated by RANKL deficiency in MALPs. In summary, our studies identified MALPs as a critical player in controlling bone remodeling during normal bone metabolism and pathological bone loss in a RANKL-dependent fashion.

Wave-digital filter circuits for single-chip 4-D light field depth-based enhancement

In four-dimensional (4-D) light field (LF) processing, 4-D linear shift-invariant filters having hyperplanar passbands are used for depth-based scene enhancement. In this paper, two low-sensitivity and low-complexity field programmable gate array (FPGA)-based digital hardware architectures for 4-D hyperplanar filters are proposed for on-chip real-time processing of LFs. Both 4-D filters are designed exploiting resonant properties of multi-dimensional passive prototype networks, and are realized as wave digital filters (WDFs). The two 4-D WDF realizations are implemented as raster-scanned processing architectures on a Xilinx Virtex 6 Sx35 FPGA with a real-time clock speed of up to 33 MHz. This corresponds to a real-time throughout of 16.8 LFs/s for an LF of size $$11 \times 11 \times 128 \times 128$$ .

You Shall Not Pass: Avoiding Spurious Paths in Shortest-Path Based Centralities in Multidimensional Complex Networks

The aggregation process may create spurious paths on the aggregated view of multidimensional (high order) complex networks. Consequently, these spurious paths may then cause shortest-path based centrality metrics to produce incorrect results, thus undermining the network centrality analysis. In this context, we propose a method built upon MultiAspect Graphs (MAGs) able to avoid taking into account spurious paths when computing centralities based on shortest paths in multidimensional complex networks. We show that, by using our proposed method, pitfalls usually associated with spurious paths resulting from aggregation in multidimensional networks can be avoided at the time of the aggregation process. As a result, shortest-path based centralities are assured to be computed correctly for multidimensional networks, without taking into account spurious paths that could otherwise lead to incorrect results. We also present case studies that show the impact of spurious paths in the computing of shortest paths and consequently of shortest-path based centralities, such as betweenness and closeness, thus illustrating the importance of this contribution.

On Throughput Optimization in Software-Defined Multi-Dimensional Space Division Multiplexing Optical Networks

The capacity expansion of a single time division multiplexing (TDM) or wavelength division multiplexing (WDM) domain is difficult to cope with long-term capacity pressure. The emergence of space division multiplexing (SDM) technology provides a new dimension for the expansion of optical network capacity, namely the spatial dimension. However, this also poses a challenge to both network control and resource optimization. Therefore, to improve the flexibility of network control, we propose an architecture of software-defined multi-dimensional SDM optical networks based on architecture on demand (AoD) nodes and multi-core fiber (MCF) links in this paper. Next, we mathematically describe the system model including a model of the SDM network, a model of multi-type service requests, and a MCF model. Then, an integer linear programming (ILP) is developed to minimize the index of multi-dimensional resources occupied by multiple types of services in the time, frequency, and space domains. To reduce inter-core crosstalk between homogeneous cores, the core classification algorithm is designed to build multi-granular network layers. Also, for adjacent heterogeneous cores, we utilize the AoD to customize different functions and use different spectrums to transmit different types of services. Finally, we propose routing and multi-dimensional resource allocation algorithms to optimize network throughput. The simulation results show that the proposed algorithms can effectively improve the throughput of the software-defined multi-dimensional SDM optical networks compared with the benchmark algorithms.

Academic Influence Aware and Multidimensional Network Analysis for Research Collaboration Navigation Based on Scholarly Big Data

Scholarly big data, which is a large-scale collection of academic information, technical data, and collaboration relationships, has attracted increasing attentions, ranging from industries to academic communities. The widespread adoption of social computing paradigm has made it easier for researchers to join collaborative research activities and share academic data more extensively than ever before across the highly interlaced academic networks. In this study, we focus on the academic influence aware and multidimensional network analysis based on the integration of multi-source scholarly big data. Following three basic relations: Researcher-Researcher, Researcher-Article, and Article-Article, a set of measures is introduced and defined to quantify correlations in terms of activity-based collaboration relationship, specialty-aware connection, and topic-aware citation fitness among a series of academic entities (e.g., researchers and articles) within a constructed multidimensional network model. An improved Random Walk with Restart (RWR) based algorithm is developed, in which the time-varying academic influence is newly defined and measured in a certain social context, to provide researchers with research collaboration navigation for their future works. Experiments and evaluations are conducted to demonstrate the practicability and usefulness of our proposed method in scholarly big data analysis using DBLP and ResearchGate data.

Egocentric network characteristics of persons with Type 1 diabetes and their relationships to perceived social support and well-being

ABSTRACT Objectives: The size of one's support network is positively related to health and well-being. It is therefore important to understand this association in people with Type 1 diabetes, as this could inform interventions. Moreover, the type of support (emotional, instrumental, informational) offered likely varies by gender of both the person seeking support and offering support. We thus examine the relationship between the composition of (perceived) social support networks and well-being in a sample of 121 persons with Type 1 diabetes. Design: An egocentric social network survey, combined with survey measures. Main outcome(s): The size and composition of support networks and well-being. Measures: Participants indicated the type of support individuals in their contact network offered and their gender, alongside measures of perceived social support and well-being. They indicated which individuals offered which types of support (emotional, instrumental, informational). Results: Perceived support was associated with the actual size of the emotional support network. Further, the size of the emotional support network was associated with well-being. Using multilevel models we examined assortment by gender in social support networks. Compared to women, men were more inclined to list the opposite gender as support, especially for emotional and informational support. Conclusion: Mapping out an individual's multidimensional support network paints a more complete picture of support than single item measures of support. We therefore recommend relying on a social network methodology to gain a more complete understanding of support networks. The findings highlight that an association exists between emotional network size and wellbeing. Given the potential implications of this finding for the quality of life of diabetes patients, it is important to establish the causality of this relationship.

Multi-Objective Multi-Dimensional Transportation: A Case Study to the Flow of the Commodities of the Main Roads to Main Nodes in the North Western Coastal Strip of Egypt

The distribution of merchandises and commodities from source towns to final destinations is a vital issue. The job of transporter’s decisions can be optimized by reformulating the transportation problem as generalization of the classical transportation problems. Multiobjective multi-dimensional transportation network is considered the extension of conventional two-dimensional transportation network and is convenient for dealing with transportation systems with multiple supply nodes, multiple demand nodes, as well as diverse modes of transportation demands or delivering multiple kinds of merchandises. In this study, we implement an improved Biogeography based optimization IBBO to the flow of the commodities of the main roads to main nodes in the North Western Coastal Strip of Egypt, where there are four main roads and three nodes. The proposed algorithm incorporates the dominance criteria to handle multiple objective functions which enable the decision maker to cover all the Pareto frontier of the problem which have a large-scale size. Numerical results were reported in order to establish the real computational burden of the proposed algorithm and to assess its convergence performances for solving real geographical problem.

Research on Differentiated Classification System of Distribution Network Based on Grading and Zoning

In order to cope with multiple pressures such as ensuring the safe operation of the distribution network, promoting market-oriented operation, and satisfying rigid investment needs, power grid companies must implement a lean management approach. In response to the lack of systematic classification and evaluation methods for the distribution network in precise investment and future planning, this paper proposes a multi-dimensional hierarchical sub-regional distribution network differentiation classification system covering provinces, cities (counties), urban power supply units, towns, and villages. In the dimensions of provinces and cities (counties), based on the data fitting analysis of per capita electricity consumption, the classification method of the development stage of the power grids is obtained, and the provinces and cities (counties) are classified into pioneering benefits, steady improvement, and excellence three stages; in the dimension of urban power supply units, based on the combination of “top-down” and “bottom-up”, the urban power supply units are divided into three categories: A+, A, and B; in the dimensions of towns and villages, according to characteristics indicators and construction models, proposed classification schemes for towns and villages. Finally, the results of an example analysis show that the differentiated classification system proposed in this paper can achieve an accurate assessment of the current development status of the distribution network, and provide important guidance for the precise investment of the distribution network in the future.

Trip Generation Prediction Based on the Convolutional Neural Network-Multidimensional Long-Short Term Memory Neural Network Model at Grid Cell Scale

The prediction of trip generation is an essential problem for effective traffic engineering and urban management. Traditional methods are on the large spatial scale (e.g. Traffic analysis Zone, TAZ), based on the single source and fewer types data. It is difficult to carry out refined research on smaller spatial units, due to the high aggregation of personal trip survey data. In addition, the experience-based models cannot easily capture complex non-linear relationship, which leads to lower accuracy. Multi-sources data provides the possibility to improve the prediction accuracy of trip generation. Based on the point of interest data (POI), more disaggregate spatial unit are subdivided, and grid-scale spatial correlations are taken into consideration. This paper proposes a Convolutional Neural Network-Multidimensional Long-short term memory neural network (CNN-MDLSTM) model to analyze the spatial correlation between trip generation and land use features, capture prominent features in a spatial range through the convolution structure and describe the spatial interaction using the sequence transfer structure of Long-short term memory neural network (LSTM). The deformed Multi-Dimensional Long-Short Term Memory neural network (MDLSTM) is used to adapt to the two-dimensional spatial relationship. Through case analysis and comparative analysis between models, it is shown that CNN-MDLSTM characterizes the quantitative law of trip generation and land use features better than the other neural network models. In addition, this study also discusses the output prediction accuracy of different land use grid cells and the impact of different land use characteristics on the prediction accuracy.