Neighborhood Relations Research Articles

Attributed graph subspace clustering with residual compensation guided by adaptive dual manifold regularization

As an important technology for recommendation system, attributed graph clustering has received extensive attention recently. Attributed graph clustering methods based on graph neural networks are the mainstream methods . However, their assumption that the attributes of adjacent nodes are similar is often not satisfied in the real world, which influences the clustering performance. To this end, this paper proposes an attributed graph subspace clustering algorithm with residual compensation guided by adaptive dual manifold regularization (ADMRGC). On the basis of the low rank representation subspace clustering (LRR) model, ADMRGC introduces attributed manifold regularization, topological manifold regularization and residual compensation, which make ADMRGC possible to utilize attributed similarity and topological similarity at the same time, thus solving the problem that traditional subspace clustering only considers attributed information. In addition, ADMRGC balances the contribution of node attributes and topology by adaptively weighting the dual manifold regularization, and uses the residual representation matrix to describe the difference between node attributed similarity and topological neighbor relationship. Therefore, ADMRGC can avoid the limitation of graph neural network models assuming that the attributes of adjacent nodes are similar. Experimental results on 7 public graph datasets show that ADMRGC can achieve the best clustering performance on both high-homophily and low-homophily datasets. Especially, for datasets with low homophily, ADMRGC as a shallow model can also achieve better clustering performance than state-of-the-art deep neural network models.

Lattice light sheet microscopy reveals 4D force propagation dynamics and leading-edge behaviors in an embryonic epithelium in Drosophila

How pulsed contractile dynamics drive the remodeling of cell and tissue topologies in epithelial sheets has been a key question in development and disease. Due to constraints in imaging and analysis technologies, studies that have described the invivo mechanisms underlying changes in cell and neighbor relationships have largely been confined to analyses of planar apical regions. Thus, how the volumetric nature of epithelial cells affects force propagation and remodeling of the cell surface in three dimensions, including especially the apical-basal axis, is unclear. Here, we perform lattice light sheet microscopy (LLSM)-based analysis to determine how far and fast forces propagate across different apical-basal layers, as well as where topological changes initiate from in a columnar epithelium. These datasets are highly time- and depth-resolved and reveal that topology-changing forces are spatially entangled, with contractile force generation occurring across the observed apical-basal axis in a pulsed fashion, while the conservation of cell volumes constrains instantaneous cell deformations. Leading layer behaviors occur opportunistically in response to favorable phasic conditions, with lagging layers "zippering" to catch up as new contractile pulses propel further changes in cell topologies. These results argue against specific zones of topological initiation and demonstrate the importance of systematic 4D-based analysis in understanding how forces and deformations in cell dimensions propagate in a three-dimensional environment.

Social and Economic Effects of Different Urban Renewal Projects on the Surrounding Settlements: An Evaluation on Bursa Küplüpınar Neighborhood

Today, many cities are experiencing transformation as a result of the chaotic structure brought about by growth. It is the physical, social, and economic inadequacies that accelerate/trigger the transformation and make it inevitable. This urban decay, defined as obsolescence, can occur not only due to spatial growth but also as a result of fragmentary and unrelated plan decisions. In this study, the aim is to reveal the physical deterioration processes that emerged in the Küplüpınar neighborhood, which is located in the center of Bursa and in the grip of a region that was transformed with different types of urban renewal projects after 2000. Also, the effects of the transformation expectation created by these processes on the identity and social structure of the region is determined. In this context, surveys and in-depth interviews were conducted with individuals who live in the neighborhood or have lived there in the past years. The obtained data were analyzed using SPSS software. In conclusion, it has been determined that the renewal projects carried out in a fragmented manner create an expectation of transformation in the surrounding settlements. Over time, this expectation leads to decrepitude, population loss, weakening of identity and neighborhood relations, and the formation.

O2SAT: Object-Oriented-Segmentation-Guided Spatial-Attention Network for 3D Object Detection in Autonomous Vehicles

Autonomous vehicles (AVs) strive to adapt to the specific characteristics of sustainable urban environments. Accurate 3D object detection with LiDAR is paramount for autonomous driving. However, existing research predominantly relies on the 3D object-based assumption, which overlooks the complexity of real-world road environments. Consequently, current methods experience performance degradation when targeting only local features and overlooking the intersection of objects and road features, especially in uneven road conditions. This study proposes a 3D Object-Oriented-Segmentation Spatial-Attention (O2SAT) approach to distinguish object points from road points and enhance the keypoint feature learning by a channel-wise spatial attention mechanism. O2SAT consists of three modules: Object-Oriented Segmentation (OOS), Spatial-Attention Feature Reweighting (SFR), and Road-Aware 3D Detection Head (R3D). OOS distinguishes object and road points and performs object-aware downsampling to augment data by learning to identify the hidden connection between landscape and object; SFR performs weight augmentation to learn crucial neighboring relationships and dynamically adjust feature weights through spatial attention mechanisms, which enhances the long-range interactions and contextual feature discrimination for noise suppression, improving overall detection performance; and R3D utilizes refined object segmentation and optimized feature representations. Our system forecasts prediction confidence into existing point-backbones. Our method’s effectiveness and robustness across diverse datasets (KITTI) has been demonstrated through vast experiments. The proposed modules seamlessly integrate into existing point-based frameworks, following a plug-and-play approach.

Дом и родина трансмигранта в фокусе современной концептуализации

The article presents a reflection on the conceptualization of the ideas of the migrant’s “home” and “homeland”. Rethinking the experience of conceptualizing these concepts seems appropriate due to its heterogeneity, which is due to the formation of the phenomenon of transnationalism at the end of the 20th century, reflecting the emergence of transnational migration. Home and homeland are considered in the structure of transnational identity as its identifiers. The theoretical framework of the analysis was the concept of the domestic researcher Z.L. Levin, which allows interpreting home and homeland as social, i.e. acquired identifications. The consequence of the reconstruction of the semantic fullness of the concepts of home and homeland is the transformation of identity and/or the formation of a trans-identity, involving a combination of elements of cultures, both the country of origin and the recipient country. The beginning of the discourse about the home of the modern migrant falls on the 80s of the XX century when the issue of its loss to migrants was being considered. In the early 1990s, an anti-centrist line of research was formed, which is, in general, a complex of nomadic studies based on the concepts of globalization, transnationalism and cross-borderism. The idea of transforming the house is being formed. The house ceases to be a “fixed structure” and loses the function of a geographical center, it becomes mobile, its topography expanding. At the same time, there is a group of scientists who deny the generalization of ideas about home and homeland. In general, when defining ‘home’, representatives of global discourse adhere to a number of its unified indicators, the most significant of which are terrain, sense of security, space, social relations of family and neighborhood. The analysis of the results of the modern conceptualization of home has shown the expansion of the semantic load of this concept and the identification of new properties of home – mobility and plurality. If before the start of transnational migration, the concepts of ‘home’ and homeland were linked together by the formulation native home, then in the conditions of fluid modernity, forming a plastic transnational identity of a migrant, these concepts began to exist separately. The concept of homeland, unlike home, has an ideological connotation. In the context of the mosaic of modern social processes and their political coloring, its conceptualization is in the process of development.

The Interpretation of the Meaning of al-Qarábah in the Qur'an and Its Contextualization with Contemporary Kinship Models (An Interpretative Approach Using the Maudhu'i Tafsir Method)

Based on the interpretation in the Qur'an, relatives (al-qurbá) are interpreted flexibly, according to the context of the conversation, both from the side of the mother's and father's relatives. However, traditional interpretations tend to emphasize the paternal family, reflected in the influence of patrilineal Arabic culture. In the contemporary era, the concept of family in anthropology has varied, including maternal or bilateral models. This study analyzes how the meaning of al-qarábah in the Qur'an with the approach of maudhu'i tafsir, tracing its conformity with the contemporary kinship model? The type of qualitative research and interpretive approach is carried out in order to be answered in depth, data collection through library research, so that the data collected is in the form of secondary data obtained from the books of scholarly interpretation. Content analysis is carried out by collecting verses related to the method of tafsir maudhu'i. As a result, the method of tafsir maudhu'i al-qarābah in Islam is divided into three levels: al-qarābah al-qarībah (very close uterine relationship), al-qarābah al-mutawassiṭah (mahram relatives), and al-qarābah al-baʿidah (non-mahram relatives). In the context of the modern family, kinship is understood through the biological dimensions (nasab/DNA), religion (brothers and sisters), and social (neighborly relationships). Fathers and mothers are considered the nucleus of the family in Islam, with mothers having primary responsibility for the upbringing, education, and inheritance of children in the event of a father's death, affirming protection and harmony in Muslim families.

Integration of Manifold Learning and Density Estimation for Fine-Tuned Face Recognition

With the rapid advancements in data analysis and the increasing complexity of high-dimensional datasets, traditional dimensionality reduction techniques like Local Linear Embedding (LLE) often face challenges in maintaining accuracy and efficiency. This research aims to overcome the limitations of LLE, specifically its reliance on the nearest neighbor concept, its inability to distinguish differences among manifold points, and its underutilization of data discrimination information. To address these issues, we propose an advanced LLE algorithm that integrates decision tree-based neighbor recognition with Gaussian kernel density estimation. Decision trees accurately determine neighboring relationships, which are then optimized using Gaussian kernel density estimation to better reflect the distribution of sample points on the manifold. The algorithm also incorporates data discrimination information to enhance classification accuracy and efficiency. Evaluations in facial recognition tasks using SVM classifiers demonstrate significant improvements. Integrating decision trees (LLE-DT) yielded accuracy gains, with LFW at 98.75%, CFP 96.10%, and Olivetti 92.18%. Gaussian density estimation (LLE-GDE) achieved further enhancements, especially in LFW (99.13%), with CFP at 96.85%, and Olivetti at 91.82%. Combining both methods (LLE-DT-GDE) led to substantial improvements: LFW 99.61%, CFP 97.23%, and Olivetti 93.56%, highlighting the synergy between decision trees and Gaussian estimation. This advanced LLE algorithm effectively addresses the limitations of traditional approaches, showing promising results in complex data processing tasks such as facial recognition. These findings suggest its potential for broader applications in fields requiring robust data analysis and classification.

A robust approach for aspect-based sentiment analysis using deep learning and domain ontologies

PurposeThe purpose of this research study is to improve sentiment analysis (SA) at the aspect level, which is accomplished through two independent goals of aspect term and opinion extraction and subsequent sentiment classification.Design/methodology/approachThe proposed architecture uses neighborhood and dependency tree-based relations for target opinion extraction, a domain–ontology-based knowledge management system for aspect term extraction, and deep learning techniques for classification.FindingsThe authors use different deep learning architectures to test the proposed approach of both review and aspect levels. It is reported that Vanilla recurrent neural network has an accuracy of 83.22%, long short-term memory (LSTM) is 89.87% accurate, Bi-LSTM is 91.57% accurate, gated recurrent unit is 65.57% accurate and convolutional neural network is 82.33% accurate. For the aspect level analysis, ρaspect comes out to be 0.712 and Δ2aspect is 0.384, indicating a marked improvement over previously reported results.Originality/valueThis study suggests a novel method for aspect-based SA that makes use of deep learning and domain ontologies. The use of domain ontologies allows for enhanced aspect identification, and the use of deep learning algorithms enhances the accuracy of the SA task.

An Intelligent Connected Vehicle Material Distribution Route Model Based on k-Center Spatial Cellular Clustering and an Improved Cockroach Optimization Algorithm

Based on the analysis of the problems in material distribution routes, we propose the idea of integrating the intelligent connected vehicle system with material distribution, and construct an intelligent connected vehicle material distribution route model based on k-center spatial cellular clustering and an improved cockroach optimization algorithm. Firstly, we set the research scope to include the distribution center, the distribution points and the geographical environment. A cellular spatial model of distribution points is constructed to quantify and visualize the neighborhood relationship between the distribution centers and distribution points. On this basis, we construct an intelligent connected vehicle material distribution route model based on the improved cockroach optimization algorithm, and the optimal material distribution center is determined by searching for the corresponding optimal distribution route of each distribution center. In the experiment, we use the concept of symmetry to design routes that start from the initial points. The route passes through the distribution point, and finally reaches the destination. In this mode, the experiment generates symmetrically round-trip routes and generates different distribution time schedules. Case studies and comparative experiments show that the proposed algorithm has a total distance cost 1.2 km lower than the distance cost generated by the Baidu Map method and 2.7 km lower than the distance cost generated by the 360 Map method. In terms of the total time cost of the proposed algorithm, it is 0.06 h lower than the time cost generated by the Baidu Map method and 0.135 h lower than the time cost generated by the 360 Map method. Compared with the commonly used Dijkstra algorithm and the A * algorithm for route optimization, our proposed algorithm also generates a lower cost than the two other types of optimization algorithms. In the case study, the distance generated by the proposed algorithm is 1.8 km lower than that of the Dijkstra algorithm, and the total time cost is 0.09 h lower than that of the Dijkstra algorithm. The distance generated by the proposed algorithm is 1.6 km lower than that of the A* algorithm, and the total time cost is 0.08 h lower than that of the A* algorithm. Meanwhile, the proposed algorithm has a lower time complexity than the two commonly used optimization algorithms. Therefore, our proposed algorithm can find the distribution route with the lowest transportation cost. Compared to the commonly used electronic maps and the optimization algorithms for distribution route planning, our proposed algorithm can output distribution routes with lower costs under the same distribution sequence, and reduce the transportation costs for intelligent connected vehicle material distribution systems to the maximum extent.

Network-based time series modeling for COVID-19 incidence in the Republic of Ireland

Network-based time series models have experienced a surge in popularity over the past years due to their ability to model temporal and spatial dependencies, arising from the spread of infectious disease. The generalised network autoregressive (GNAR) model conceptualises time series on the vertices of a network; it has an autoregressive component for temporal dependence and a spatial autoregressive component for dependence between neighbouring vertices in the network. Consequently, the choice of underlying network is essential. This paper assesses the performance of GNAR models on different networks in predicting COVID-19 cases for the 26 counties in the Republic of Ireland, over two distinct pandemic phases (restricted and unrestricted), characterised by inter-county movement restrictions. Ten static networks are constructed, in which vertices represent counties, and edges are built upon neighbourhood relations, such as railway lines. We find that a GNAR model based on the fairly sparse Economic hub network explains the data best for the restricted pandemic phase while the fairly dense 21-nearest neighbour network performs best for the unrestricted phase. Across phases, GNAR models have higher predictive accuracy than standard ARIMA models which ignore the network structure. For county-specific predictions, in pandemic phases with more lenient or no COVID-19 regulation, the network effect is not quite as pronounced. The results indicate some robustness to the precise network architecture as long as the densities of the networks are similar. An analysis of the residuals justifies the model assumptions for the restricted phase but raises questions regarding their validity for the unrestricted phase. While generally performing better than ARIMA models which ignore network effects, there is scope for further development of the GNAR model to better model complex infectious diseases, including COVID-19.

Collaborative denoised graph contrastive learning for multi-modal recommendation

Graph neural networks, with their capacity to capture complex hierarchical relations, are extensively employed in multi-modal recommendation. Previous graph-based multi-modal recommendation studies primarily focus on integrating multi-modal features that capture the neighbor relations as auxiliary information. However, such methods heavily rely on graph structure properties for collaborative relations. Furthermore, while the massive implicit feedbacks alleviate the data sparsity issue, the drawback is that they are not as reliable in accurately reflecting users true interests. We propose a Collaborative Denoised Graph Contrastive Learning framework named CDGCL for multi-modal recommendation. Specifically, we present a novel modality-aware item representation with contrastive learning to capture the modality-aware collaborative relations. Besides, we develop a Multi-Policy Denoised module (MPD) to filter out irrelevant interactions. Extensive experiments that include cold-start and warm-start experimental scenarios demonstrate the superiority of CDGCL over baselines.

Multi-label feature selection using self-information in divergence-based fuzzy neighborhood rough sets

Multi-label feature selection corresponds to pattern recognition and knowledge mining, and its application has been expanded to different scenarios. As an excellent processing platform for uncertain and ambiguous information, divergence-based fuzzy rough sets (Div-FRSs) have been proposed and applied to feature selection. However, there are three critical problems to be solved when applying Div-FRSs to multi-label learning. The first is how to effectively dispose the noise produced by features in multi-label data. The second is how to synthetically consider the relevance among all labels. The last is how to thoroughly mine the uncertainty brought by upper approximations neglected in Div-FRSs existing researches. To address these issues, this study presents a new divergence-based fuzzy neighborhood rough set model (Div-FNRSs) for multi-label learning using self-information. First, the divergence-based fuzzy neighborhood relation and class are gradually raised to manage the noise in multi-label data, and fuzzy decision is introduced to dispose all labels as a whole. Combining them together, a new model Div-FNRSs is constructed. Then, divergence-based fuzzy neighborhood self-information containing upper approximations and lower approximations is designed to depict distinguishing ability of features through three-level uncertainty measure establishment and granulation property exploration. Furthermore, feature significance for choosing the optimal features is given and it motivates a heuristic feature-selection algorithm DivFNSI-FS. Finally, data experiments are completed to validate DivFNSI-FS effectiveness with six state-of-the-art multi-label feature selection approaches on fourteen multi-label datasets. A conclusion can be drawn that DivFNSI-FS outperforms existing algorithms to obtain better performance on eight commonly-used evaluation indexes.

A lie group semi-supervised FCM clustering method for image segmentation

As an unsupervised clustering method with low overhead, Fuzzy C-means (FCM) clustering has been widely used in a variety of image segmentation tasks. However, existing FCM clustering methods are sensitive to image noises and are either suffer from losing of image detail or falling into local optima in identifying cluster centers. Aiming at these problems, this paper proposes a Lie group semi-supervised FCM (LieSSFCM) clustering method for image segmentation. The method maps the input image from Euclidean space to Lie group manifold by representing each image pixel as a matrix Lie group and calculates geodesic distances between group elements and cluster centers on Lie group manifold. Prior information of the image and neighborhood relationships of pixels are used to guide the initialization and constrain the update of cluster centers and the corresponding fuzzy membership matrix. The proposed LieSSFCM has been validated against two medical image datasets and was compared with seven FCM clustering methods. Experimental results along with a systematic evaluation demonstrated that the method was superior in segmentation accuracy both visually and statistically, robustness to noises, adaptability to different tasks, and stability while maintaining a moderate computational complexity.

Galor: Global view assisted localized fine-grained routing for LEO satellite networks

Low Earth Orbit (LEO) satellite networks have been expected to provide global coverage for Internet services with immediacy requirements. The dynamics of an LEO satellite network topology induce the challenges of achieving efficient content retrieval. This article takes an initial step toward achieving efficient content retrieval in LEO satellite networks from the routing perspective. We start with investigating the topology characteristics of LEO satellite networks in terms of the deterministic neighbor relation and the intermittent inter-satellite links. We then propose a Global view Assisted Localized fine-grained Routing (GALOR), which is an information-centric routing mechanism customized to LEO satellite networks. Specifically, GALOR disseminates the link state within a predefined range instead of the entire constellation, incurring less convergence time and control overhead. Therefore, GALOR can calculate the routing table (to guide interest forwarding) based on the local link state and the global neighbor relations. Moreover, GALOR improves the forwarding method of the information-centric routing by reconstructing a failed Pending Interest Table (PIT) entries in response to occasional link failures. Our packet-level experiments show that GALOR outperforms state-of-the-art mechanisms (up to 103.4%) in terms of average packet delivery ratio in content-sharing.

Joint Anchor Graph Embedding and Discrete Feature Scoring for Unsupervised Feature Selection.

The success of existing unsupervised feature selection (UFS) methods heavily relies on the assumption that the intrinsic relationships among original high-dimensional (HD) data samples exist in the discriminative low-dimension (LD) subspace. However, previous UFS methods commonly construct pairwise graphs and employ l2,1 -norm regularization to severally preserve the local structure and calculate the score of features, which is computationally complex and easy to get stuck into local optimum, so that those approaches cannot be applied in dealing with large-scale datasets in practice. To overcome this challenge, we propose a novel UFS method, in which a novel anchor graph embedding paradigm is designed to extract the local neighborhood relationships among data samples by reducing the computational complexity of graph construction to be linear in the number of data. Moreover, to improve the optimality of selected features as well as the performance of downstream tasks, we propose a discrete feature scoring mechanism, which imposes orthogonal l2,0 -norm constraints on learned projections, in order to enhance the distinction of feature scores as well as reduce the probability of falling into local optimum. In addition, solving the proposed nonconvex and nonsmooth NP-hard problem is challenging, and we present an efficient optimization algorithm to address it and acquire a closed-form solution of the transformation matrix. Extensive experiments demonstrate the effectiveness and efficiency of the proposed UFS by comparison with several state-of-the-art approaches to clustering and image segmentation tasks.

Markov-Embedded Affinity Learning with Connectivity Constraints for Subspace Clustering

Subspace clustering algorithms have demonstrated remarkable success across diverse fields, including object segmentation, gene clustering, and recommendation systems. However, they often face challenges, such as omitting cluster information and the neglect of higher-order neighbor relationships within the data. To address these issues, a novel subspace clustering method named Markov-Embedded Affinity Learning with Connectivity Constraints for Subspace Clustering is proposed. This method seamlessly embeds Markov transition probability information into the self-expression, leveraging a fine-grained neighbor matrix to uncover latent data structures. This matrix preserves crucial high-order local information and complementary details, ensuring a comprehensive understanding of the data. To effectively handle complex nonlinear relationships, the method learns the underlying manifold structure from a cross-order local neighbor graph. Additionally, connectivity constraints are applied to the affinity matrix, enhancing the group structure and further improving the clustering performance. Extensive experiments demonstrate the superiority of this novel method over baseline approaches, validating its effectiveness and practical utility.

Community Inclusion Experiences While Establishing Community Mental Health Clubhouses in Taiwan: Perspectives from Mental Health Professionals.

Community inclusion is a human right for all people, including people with mental illness. It is also an important part of individualized support to enable people experiencing mental illness to live in their preferred communities and to recover. In Taiwan, no study has investigated the social experiences of healthcare professionals. To fill this knowledge gap and inform ongoing mental health system strengthening, this study examined the not-in-my-backyard (NIMBY) phenomenon observed while establishing community mental health clubhouses in Taiwan and corresponding experiences of community inclusion. Data were collected through semi-structured interviews of 16 purposively sampled frontline healthcare professionals from Taiwanese mental health clubhouses. Data were analyzed using qualitative content analysis. Two themes were identified: "NIMBY phenomenon: Community residents' resistance to clubhouses" and "Measures adopted by the clubhouse for community inclusion". Two categories with eleven subcategories emerged. The findings demonstrate the following conclusions. First, the NIMBY phenomenon is related to the stigmatization and discrimination faced by individuals with mental disorders in society. Second, in Asian societies, mental disorders are associated with a particular social and cultural context. Third, the fear and rejection of individuals with mental disorders deepen their social exclusion. Forth, community mental health clubhouse models employ seven strategic approaches to community inclusion, listed as follows: capacity building for individuals, direct interaction with the public through community activities, provision of community services, life skills training, repairing and managing neighborhood relationships, assisting individuals in obtaining community employment, and conducting social education for community residents. Clearly, we need to combat social exclusion of people with mental illness and promote inclusive and accessible services and systems across sectors.

Regularisation constrained denoising discriminant least squares regression for image classification

Discriminative Least Squares Regression (DLSR) is an algorithm that utilizes ε-draggings to expand the distance between classes to better solve multi-classification problems. However, this method increases the differences in regression targets within classes during the classification process, and noise in the samples adversely affects its performance. To address the above problems, we propose a novel method called Regularisation Constrained Denoising Discriminant Least Squares Regression (RCDDLSR). Firstly, the idea of matrix decomposition is introduced and sparse constraints are imposed on the noise matrix for noise elimination, thus helping to maintain the manifold structure of the raw data. Secondly, the neighbourhood relationship between samples of the same category is ensured through graph regularisation to avoid overfitting. In addition, we introduce low-rank constraints in the labelling space to better capture the local structure of the regression target within the class. Finally, our experiments show that the method performs better in various types of image datasets compared to other different algorithms.

“Maybe he’s thinking something on the inside but is not showing it to us”: The Dynamics of Neighborhood Relations between Azerbaijani and Armenian Communities in a Multiethnic Region in Georgia after the Nagorno-Karabakh War in 2020

This paper explores the specifics of post-conflict neighborhood relations in the Azerbaijani and Armenian communities in Kvemo Kartli, one of the most ethnically mixed regions in Georgia. The diverse ethnic composition of the region and its historical and political background demonstrate that the non-homogenous, multiethnic local communities coexist, coping with post-conflict experiences. Drawing on ethnographic research conducted between 2020 and 2022, including interviews and field observations, this paper presents how the outbreak of the Nagorno-Karabakh war influenced contemporary Azerbaijani-Armenian relations in Georgia. The results of author's research indicate that local communities developed contrasting approaches and strategies while still maintaining neighborly relations involving antagonistic tolerance. The article poses the question of whether relations in the community are based on the komshuluk system, or whether it is rather the case of antagonistic tolerance. It also reveals in what situations conflicts occur and in what situations good neighborly relations are nurtured. Therefore, the research results also fill a gap in the studies on interethnic relations in Georgia after the Nagorno-Karabakh war. This is also the first study that uses the aforementioned theoretical framework to analyze this particular area in Georgia.

Attribute reduction for heterogeneous data based on monotonic relative neighborhood granularity

The neighborhood rough set model serves as an important tool for handling attribute reduction tasks involving heterogeneous attributes. However, measuring the relationship between conditional attributes and decision in the neighborhood rough set model is a crucial issue. Most studies have utilized neighborhood information entropy to measure the relationship between attributes. When using neighborhood conditional information entropy to measure the relationships between the decision and conditional attributes, it lacks monotonicity, consequently affecting the rationality of the final attribute reduction subset. In this paper, we introduce the concept of neighborhood granularity and propose a new form of relative neighborhood granularity to measure the relationship between the decision and conditional attributes, which exhibits monotonicity. Moreover, our approach for measuring neighborhood granularity avoids the logarithmic function computation involved in neighborhood information entropy. Finally, we conduct comparative experiments on 12 datasets using two classifiers to compare the results of attribute reduction with six other attribute reduction algorithms. The comparison demonstrates the advantages of our measurement approach.