Abstract Identifying communities in multilayer networks is crucial for understanding the structural dynamics of complex systems. Traditional community detection algorithms often overlook the presence ...of overlapping edges within communities, despite the potential significance of such relationships. In this work, we introduce a novel modularity measure designed to uncover communities where nodes share specific multiple facets of connectivity. Our approach leverages a null network, an empirical layer of the multiplex network, not a random network, that can be one of the network layers or a complement graph of that, depending on the objective. By analyzing real-world social networks, we validate the effectiveness of our method in identifying meaningful communities with overlapping edges. The proposed approach offers valuable insights into the structural dynamics of multiplex systems, shedding light on nodes that share similar multifaceted connections.
With the increasing complexity of production technologies, alarm management becomes more and more important in industrial process control. The overall safety of the plant relies heavily on the ...situation-aware response time of the staff. This kind of awareness has to be supported by a state-of-the-art alarm management system, which requires broad and up-to-date process-relevant knowledge. The proposed method provides a solution when such information is not fully available. With the utilization of machine learning algorithms, a real-time event scenario prediction can be gained by comparing the frequent event patterns extracted from historical event-log data with the actual online data stream. This study discusses an integrated solution, which combines sequence compression and sequence alignment to predict the most probable alarm progression. The effectiveness and limitations of the proposed method are tested using the data of an industrial delayed-coker plant. The results confirm that the presented parameter-free method identifies the characteristic patternsoperational statesand their progression with high confidence in real time, suggesting it for a wider adoption for sequence analysis.
Vibrations in road vehicles cause several harmful effects, health problems can occur for the passengers, and mechanical damage can occur to the vehicle components. Given the health, safety, and ...financial issues that arise, keeping the road network in good condition and detecting road defects as early as possible requires an extensive monitoring system. Related to this, our study presents the development of hardware and software for a low-cost, multi-sensor road quality monitoring system for passenger vehicles. The developed monitoring system can classify road sections according to their quality parameters into four classes. In order to detect vibrations in the vehicle, accelerometers and gyroscope sensors are installed at several points. Then, a machine learning-based soft-sensor development is introduced. Besides noise filtering, each data point is resampled by spatial frequency to reduce the velocity dependence. Subsequently, a decision tree-based classification model is trained using features from the power spectrum and principal component analysis. The classification algorithm is validated and tested with measurement data in a real-world environment. In addition to reviewing the accuracy of the model, we examine the correlation of the data measured in the cabin and on the suspension to see how much additional information is provided by the sensor on the axle.
With the ever-increasing use of sensor technologies in industrial processes and more data becoming available to engineers, the fault detection and isolation activities in the context of process ...monitoring have gained significant momentum in recent years. A statistical procedure frequently used in this domain is principal component analysis (PCA), which can reduce the dimensionality of large data sets without compromising the information content. While most process monitoring methods offer satisfactory detection capabilities, understanding the root cause of malfunctions and providing the physical basis for their occurrence have been challenging. The relatively new sparse PCA techniques represent a further development of the PCA in which not only the data dimension is reduced but also the data are made more interpretable, revealing clearer correlation structures among variables. Hence, taking a step forward from classical fault detection methods, in this work, a decentralized monitoring approach is proposed based on a sparse algorithm. The resulting control charts reveal the correlation structures associated with the monitored process and facilitate a structural analysis of the occurred faults. The applicability of the proposed method is demonstrated using data generated from the simulation of the benchmark vinyl acetate process. It is shown that the sparse principal components, as a foundation to a decentralized multivariate monitoring framework, can provide physical insight toward the origins of process faults.
Network analysis can be applied to understand organizations based on patterns of communication, knowledge flows, trust, and the proximity of employees. A multidimensional organizational network was ...designed, and association rule mining of the edge labels applied to reveal how relationships, motivations, and perceptions determine each other in different scopes of activities and types of organizations. Frequent itemset-based similarity analysis of the nodes provides the opportunity to characterize typical roles in organizations and clusters of co-workers. A survey was designed to define 15 layers of the organizational network and demonstrate the applicability of the method in three companies. The novelty of our approach resides in the evaluation of people in organizations as frequent multidimensional patterns of multilayer networks. The results illustrate that the overlapping edges of the proposed multilayer network can be used to highlight the motivation and managerial capabilities of the leaders and to find similarly perceived key persons.
The Erasmus Programme is the biggest collaboration network consisting of European Higher Education Institutions (HEIs). The flows of students, teachers and staff form directed and weighted networks ...that connect institutions, regions and countries. Here, we present a linked and manually verified dataset of this multiplex, multipartite, multi-labelled, spatial network. We enriched the network with institutional socio-economic data from the European Tertiary Education Register (ETER) and the Global Research Identifier Database (GRID). We geocoded the headquarters of institutions and characterised the attractiveness and quality of their environments based on Points of Interest (POI) data. The linked datasets provide relevant information to grasp a more comprehensive understanding of the mobility patterns and attractiveness of the institutions.
Countries have to work out and follow tailored strategies for the achievement of their Sustainable Development Goals. At the end of 2018, more than 100 voluntary national reviews were published. The ...reviews are transformed by text mining algorithms into networks of keywords to identify country-specific thematic areas of the strategies and cluster countries that face similar problems and follow similar development strategies. The analysis of the 75 VNRs has shown that SDG5 (gender equality) is the most discussed goal worldwide, as it is discussed in 77% of the analysed Voluntary National Reviews. The SDG8 (decent work and economic growth) is the second most studied goal, With 76 %, while the SDG1 (no poverty) is the least focused goal, it is mentioned only in 48 % of documents and the SDG10 (reduced inequalities) in 49 %. The results demonstrate that the proposed benchmark tool is capable of highlighting what kind of activities can make significant contributions to achieve sustainable developments.
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•Voluntary national reviews can be structured by text mining and network analysis.•The network of the keywords structures the issues of sustainability.•The focal-points can be clustered based on transitive similarity of the words.•Layer overlaps in the multiplex network highlight benchmarking opportunities.•The need for international cooperation to achieve the SDGs has been demonstrated.
Clustering high dimensional data and identifying central nodes in a graph are complex and computationally expensive tasks. We utilize k-nn graph of high dimensional data as efficient representation ...of the hidden structure of the clustering problem. Initial cluster centers are determined by graph centrality measures. Cluster centers are fine-tuned by minimizing fuzzy-weighted geodesic distances. The shortest-path based representation is parallel to the concept of transitive closure. Therefore, our algorithm is capable to cluster networks or even more complex and abstract objects based on their partially known pairwise similarities.
The algorithm is proven to be effective to identify senior researchers in a co-author network, central cities in topographical data, and clusters of documents represented by high dimensional feature vectors.
Frequent sequence pattern mining is an excellent tool to discover patterns in event chains. In complex systems, events from parallel processes are present, often without proper labelling. To identify ...the groups of events related to the subprocess, frequent sequential pattern mining can be applied. Since most algorithms provide too many frequent sequences that make it difficult to interpret the results, it is necessary to post-process the resulting frequent patterns. The available visualisation techniques do not allow easy access to multiple properties that support a faster and better understanding of the event scenarios. To answer this issue, our work proposes an intuitive and interactive solution to support this task, introducing three novel network-based sequence visualisation methods that can reduce the time of information processing from a cognitive perspective. The proposed visualisation methods offer a more information rich and easily understandable interpretation of sequential pattern mining results compared to the usual text-like outcome of pattern mining algorithms. The first uses the confidence values of the transitions to create a weighted network, while the second enriches the adjacency matrix based on the confidence values with similarities of the transitive nodes. The enriched matrix enables a similarity-based Multidimensional Scaling (MDS) projection of the sequences. The third method uses similarity measurement based on the overlap of the occurrences of the supporting events of the sequences. The applicability of the method is presented in an industrial alarm management problem and in the analysis of clickstreams of a website. The method was fully implemented in Python environment. The results show that the proposed methods are highly applicable for the interactive processing of frequent sequences, supporting the exploration of the inner mechanisms of complex systems.
Since the declaration of Sustainable Development Goals (SDGs) in 2015, countries have begun developing and strategizing their national pathways for effective implementation of the 2030 Agenda. The ...sustainable development targets set out how the world’s nations must move forward so that sustainable development is not an ideal vision but a workable, comprehensive environmental, economic, and social policy. This work aims to analyze the state of progress towards achieving sustainable development goals for each country. In addition to the static presentation of the achievements that countries can present, the changes over time are also compared, allowing countries to be grouped according to the current states. A sophisticated SDG performance measurement tool has been developed to support this analysis, which automatically processes the entire UN Global SDG Indicators database with exploratory data analysis, frequent item mining, and network analysis supported. Based on the trend analysis of the percentiles, the values of the indicators achievable by 2030 are also derived. The analyzes were performed based on the time-series data of 1319 disaggregated official SDG indicators.
Most of the world countries have achieved the greatest success in SDG12 and SDG10 since the declaration of the 2030 Agenda. In the field of climate change (SDG13), 26 countries can count on significant achievements. However, SDG6, SDG2, and SDG1 face significant challenges globally, as they have typically seen minor progress in recent years. Examined at the indicator level, indicators 1.4.1, 5.6.2, 6.b.1, 10.7.2, and 15.4.2 improved in all countries of the world, while indicators 2.a.1, 9.4.1, 2.1.1, 2.1. and 12.b.1 have deteriorated predominantly. According to the forecast for 2030, Australia and the United States can reduce their per capita CO2 emissions, while some countries in Africa, Asia, and the Middle East are expected to increase their emissions.
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