Community Detection Methodologies Research Articles

Transboundary Ecological Network Communities based on natural protected areas: A case study of the Mekong River Basin, Asia

Natural protected areas (NPAs) around the world play a crucial role in preserving Earth's biodiversity. Ecological networks can be used to identify the close connections between NPAs. The Mekong River, one of the most crucial international rivers in Southeast Asia, boasts a temperate climate and abundant water resources, serving as a hotspot for global biodiversity and a densely populated area with a complex macro-ecosystem. This study, based on the characteristics of cross-border ecosystems in the Mekong River Basin, introduces the concept of “Ecological Network Communities” (ENCs) and demonstrates its foundational construction methodology: firstly, the Minimum Cumulative Resistance (MCR) model is employed to construct ecological networks based on NPAs, followed by applying Clauset-Newman-Moore (CNM) algorithm for community detection within the river basin. The results of the study indicate the presence of deep structures within the ecological networks of the Mekong River Basin and ENCs are observed to exhibit variations over time. The study delves into the complexities of transboundary ecological networks, offering an effective methodology for community detection and uncovering the "Community Merging Effect” observed during the research. It emphasizes the need for constructing ecological networks and community-based management of protected areas, providing new directions for cross-national environmental cooperation and ecosystem management oriented towards complexity.

Hybrid Filtering and Probabilistic Techniques for Privacy-Preserving Community Detection in OSNs

Online Social Networks (OSNs) face the major challenge of protecting participant's privacy, due to the high dimensionality and volume of the data. In real-time social networks, where hundreds of personal details of people are shared every day, there remains a significant threat to privacy. Privacy preservation is challenging for a community detection problem due to the high computational complexity and memory requirements, especially in larger real-world OSN graphs. Although weighted nodes provide better results, as they allow capturing the frequencies of the values, the privacy preservation of sensitive attributes such as specific profiles becomes harder compared to these models. This problem can result in queries and subsequent learnings from social network profiles of specific individuals, which may be of personal, political or otherwise concern. Online social networks (OSNs) grapple with significant privacy challenges due to the extensive dimensions and vast quantities of data involved. This research fills the void in current privacy-preserving community detection methodologies, which face problems in computational complexity and memory usage in large-scale OSNs. The proposed framework seeks to bolster privacy preservation through a comprehensive multi-step process. Data filtering uses a blended data filter system to remove outliers and irrelevant data, thus enhancing the quality of the input data. Density-Oriented Clustering phase employs a density-oriented clustering model to identify communities, with each cluster representing a distinct community. Privacy Preservation component introduces a new privacy preservation technique for sensitive OSN attributes, surpassing existing k-anonymization methods. The developed density-based social network community detection model and its novel privacy-preserving scheme are evaluated using the datasets Yelp, Football, Zachary and Dolphin from the SNAP dataset. Experimental results on these datasets embed a comprehensive evaluation based on the order of each node and the graph-based networks, where each node is laden with weights as proximity values, indicating the semantic proximity between communities and individuals. The proposed framework employs the normalized mutual information (NMI), modularity (Q), Rand index and runtime measurements to demonstrate widespread advantages in the multi-dimensional functional space, including greater accuracy, cluster compatibility, and computational tractability over the existing prominent traditional models for OSNs.

Exploring dynamic urban mobility patterns from traffic flow data using community detection

ABSTRACT The rise of data from smart city services and the emergence of advanced algorithms have emphasized the need for a deeper understanding of the underlying patterns of urban mobility and the potential opportunities for more efficient urban planning and policymaking. By identifying communities with similar mobility patterns, researchers can gain better insights into how people move within and between different regions. Traditional community detection methodologies mainly focused on identifying geographic communities defined by shared locations. However, this perspective overlooks the broader definition of communities. Communities can also emerge from shared social interests, collective actions, and activity patterns. This implies that geographically disparate areas might exhibit similar patterns, which is particularly relevant in the study of mobility pattern similarity. Rather than focusing on regions with strong spatial interactions, this study aims to identify regions that show more similarity to each other than to other areas. Such similarities may indicate parallel urban functionalities, which are essential for effective urban planning and policymaking. To bridge this gap, our study introduces a customized community detection algorithm that employs Dynamic Time Warping (DTW) to quantitatively assess the similarity in mobility patterns between different communities. This advanced approach not only improves the identification of comparable mobility patterns but also demonstrates remarkable flexibility, broadening its application to various other social phenomena. The results demonstrate the effectiveness of the proposed model in capturing complex mobility patterns across different locations and days of the week.

The world trade network: country centrality and the COVID-19 pandemic

International trade is based on a set of complex relationships between different countries that can be modelled as an extremely dense network of interconnected agents. On the one hand, this network might favour the economic growth of countries, but on the other, it can also favour the diffusion of diseases, such as COVID-19. In this paper, we study whether, and to what extent, the topology of the trade network can explain the rate of COVID-19 diffusion and mortality across countries. We compute the countries’ centrality measures and we apply the community detection methodology based on communicability distance. We then use these measures as focal regressors in a negative binomial regression framework. In doing so, we also compare the effects of different measures of centrality. Our results show that the numbers of infections and fatalities are larger in countries with a higher centrality in the global trade network.

Role detection in bicycle-sharing networks using multilayer stochastic block models

AbstractIn urban systems, there is an interdependency between neighborhood roles and transportation patterns between neighborhoods. In this paper, we classify docking stations in bicycle-sharing networks to gain insight into the human mobility patterns of three major cities in the United States. We propose novel time-dependent stochastic block models, with degree-heterogeneous blocks and either mixed or discrete block membership, which classify nodes based on their time-dependent activity patterns. We apply these models to (1) detect the roles of bicycle-sharing stations and (2) describe the traffic within and between blocks of stations over the course of a day. Our models successfully uncover work blocks, home blocks, and other blocks; they also reveal activity patterns that are specific to each city. Our work gives insights for the design and maintenance of bicycle-sharing systems, and it contributes new methodology for community detection in temporal and multilayer networks with heterogeneous degrees.

A systematic review and bibliometric analysis of community detection methodologies in dynamic networks

A systematic review and bibliometric analysis of community detection methodologies in dynamic networks

Analyzing the Roles and Competence Demand for Digitalization in the Oil and Gas 4.0 Era

The rapidly moving technological advances have forced key industrial sectors to accelerate their digital transformation processes. Despite the growing interest of organizations on investing in technological advances, the Oil and Gas (O&G) industry is losing ground on the digitalization race. Except from the technological inhibitors, the relatively slow transition is related to human capital and the lack of skillful workforce able to fulfill emerging digital job positions. Additionally, the empirical evidence indicates a knowledge gap regarding the current demand for O&G digital experts and the associated responsibilities and competencies. The objective of this study is two-fold: First, we investigate the status of the O&G labor market digital career opportunities. Second, we shed light on the most prominent digital job roles and the corresponding skillsets, so as to design competence maps with interrelated technological skills. To meet our objectives, we analyzed 1999 job openings from three well-known O&G job boards through a framework augmented with natural language processing and graph theory community detection methodologies. The findings for emerging technology trends constitute an empirical benchmark for a wide range of target groups that are interested in acquiring knowledge related to the state-of-the-art digital demand and the endorsed competencies in the uprising O&G digitalized era.

A Distributed Bagging Ensemble Methodology for Community Prediction in Social Networks

Presently, due to the extended availability of gigantic information networks and the beneficial application of graph analysis in various scientific fields, the necessity for efficient and highly scalable community detection algorithms has never been more essential. Despite the significant amount of published research, the existing methods—such as the Girvan–Newman, random-walk edge betweenness, vertex centrality, InfoMap, spectral clustering, etc.—have virtually been proven incapable of handling real-life social graphs due to the intrinsic computational restrictions that lead to mediocre performance and poor scalability. The purpose of this article is to introduce a novel, distributed community detection methodology which in accordance with the community prediction concept, leverages the reduced complexity and the decreased variance of the bagging ensemble methods, to unveil the subjacent community hierarchy. The proposed approach has been thoroughly tested, meticulously compared against different classic community detection algorithms, and practically proven exceptionally scalable, eminently efficient, and promisingly accurate in unfolding the underlying community structure.

A Systematic Review and Bibliometric Analysis of Community Detection Methodologies in Dynamic Networks

With the growing use of the internet, the number of relationships between individuals has increased in large numbers. This has necessarily paved the way for community detection, which is one of the...

A Distributed Hybrid Community Detection Methodology for Social Networks

Nowadays, the amount of digitally available information has tremendously grown, with real-world data graphs outreaching the millions or even billions of vertices. Hence, community detection, where groups of vertices are formed according to a well-defined similarity measure, has never been more essential affecting a vast range of scientific fields such as bio-informatics, sociology, discrete mathematics, nonlinear dynamics, digital marketing, and computer science. Even if an impressive amount of research has yet been published to tackle this NP-hard class problem, the existing methods and algorithms have virtually been proven inefficient and severely unscalable. In this regard, the purpose of this manuscript is to combine the network topology properties expressed by the loose similarity and the local edge betweenness, which is a currently proposed Girvan–Newman’s edge betweenness measure alternative, along with the intrinsic user content information, in order to introduce a novel and highly distributed hybrid community detection methodology. The proposed approach has been thoroughly tested on various real social graphs, roundly compared to other classic divisive community detection algorithms that serve as baselines and practically proven exceptionally scalable, highly efficient, and adequately accurate in terms of revealing the subjacent network hierarchy.

Structure Detection in Mixed-Integer Programs

Despite vast improvements in computational power, many large-scale optimization problems involving integer variables remain difficult to solve. Certain classes, however, can be efficiently solved by exploiting special structure. One such structure is the singly bordered block-diagonal (BBD) structure that lends itself to Dantzig-Wolfe decomposition, Lagrangian relaxation, and branch and price. We start by introducing a new measure of goodness to capture desired features in BBD structures such as granularity of the structure, homogeneity of the block sizes, and isomorphism of the blocks. We then use it to propose a new approach to identify the best BBD structure inherent in the constraint matrix. The main building block of the proposed approach is the use of a community detection methodology in lieu of graph/hypergraph partitioning methods to alleviate one major drawback of the existing approaches in the literature: predefining the number of blocks. When tested on MIPLIB2003/2010 instances and compared against the state-of-the-art technique, the proposed algorithm is found to identify very good structures and require shorter CPU time to reach comparable bounds, in most cases.

From the User to the Medium: Neural Profiling Across Web Communities

Online communities provide a unique way for individuals to access information from those in similar circumstances, which can be critical for health conditions that require daily and personalized management. As these groups and topics often arise organically, identifying the types of topics discussed is necessary to understand their needs. As well, these communities and people in them can be quite diverse, and existing community detection methods have not been extended towards evaluating these heterogeneities. This has been limited as community detection methodologies have not focused on community detection based on semantic relations between textual features of the user-generated content. Thus here we develop an approach, NeuroCom, that optimally finds dense groups of users as communities in a latent space inferred by neural representation of published contents of users. By embedding of words and messages, we show that NeuroCom demonstrates improved clustering and identifies more nuanced discussion topics in contrast to other common unsupervised learning approaches.

Emotional community detection in social networks

It is vastly acknowledged that analyzing social networks is a very challenging research area. Take as a striking example the organization of vertices in clusters, with many edges joining vertices of the same cluster and comparatively few edges joining vertices of different clusters. This comprises a fundamental aspect, which concerns the detection of user communities. In certain fields such as sociology and computer science where interactions and associations are often represented in the form of graphs, detecting communities is of vital importance. This paper addresses the need for an efficient and innovative methodology for community detection that will also leverage users’ behavior on emotional level. Ekman emotional scale is the key point with which the methodology analyzes user’s tweets in order to determine their emotional behavior. Consequently, the derived communities are estimated with the use of three different metrics, while the weighted version of a modularity community detection algorithm is utilized. There is substantial evidence indicating that our proposed methodology creates influential enough communities.

Community Detection and Classification in Hierarchical Stochastic Blockmodels

In disciplines as diverse as social network analysis and neuroscience, many large graphs are believed to be composed of loosely connected smaller graph primitives, whose structure is more amenable to analysis We propose a robust, scalable, integrated methodology for community detection and community comparison in graphs. In our procedure, we first embed a graph into an appropriate Euclidean space to obtain a low-dimensional representation, and then cluster the vertices into communities. We next employ nonparametric graph inference techniques to identify structural similarity among these communities. These two steps are then applied recursively on the communities, allowing us to detect more fine-grained structure. We describe a hierarchical stochastic blockmodel-namely, a stochastic blockmodel with a natural hierarchical structure-and establish conditions under which our algorithm yields consistent estimates of model parameters and motifs, which we define to be stochastically similar groups of subgraphs. Finally, we demonstrate the effectiveness of our algorithm in both simulated and real data. Specifically, we address the problem of locating similar sub-communities in a partially reconstructed Drosophila connectome and in the social network Friendster.

Discovering Social Community Structures Based on Human Mobility Traces

We consider a community detection problem in a social network. A social network is composed of smaller communities; that is, a society can be partitioned into different social groups in which the members of the same group maintain stronger and denser social connections than individuals from different groups. In other words, people in the same community have substantially interdependent social characteristics, indicating that the community structure may facilitate understanding human interactions as well as individual’s behaviors. We detect the social groups within a network of mobile users by analyzing the Bluetooth-based encounter history from a real-life mobility dataset. Our community detection methodology focuses on designing similarity measurements that can reflect the degree of social connections between users by considering tempospatial aspects of human interactions, followed by clustering algorithms. We also present two evaluation methods for the proposed schemes. The first method relies on the natural properties of friendship, where the longevity, frequency, and regularity characteristics of human encounters are considered. The second is a movement-prediction-based method which is used to verify the social ties between users. The evaluation results show that the proposed schemes can achieve high performance in detecting the social community structure.

Achieving Exact Cluster Recovery Threshold via Semidefinite Programming: Extensions

Resolving a conjecture of Abbe, Bandeira and Hall, the authors have recently shown that the semidefinite programming (SDP) relaxation of the maximum likelihood estimator achieves the sharp threshold for exactly recovering the community structure under the binary stochastic block model of two equal-sized clusters. The same was shown for the case of a single cluster and outliers. Extending the proof techniques, in this paper it is shown that SDP relaxations also achieve the sharp recovery threshold in the following cases: (1) Binary stochastic block model with two clusters of sizes proportional to network size but not necessarily equal; (2) Stochastic block model with a fixed number of equal-sized clusters; (3) Binary censored block model with the background graph being Erd\H{o}s-R\'enyi. Furthermore, a sufficient condition is given for an SDP procedure to achieve exact recovery for the general case of a fixed number of clusters plus outliers. These results demonstrate the versatility of SDP relaxation as a simple, general purpose, computationally feasible methodology for community detection.

An interactive graphical method for community detection in network data

The detection of community structures within network data is a type of graph analysis with increasing interest across a broad range of disciplines. In a network, communities represent clusters of nodes that exhibit strong intra-connections or relationships among nodes in the cluster. Current methodology for community detection often involves an algorithmic approach, and commonly partitions a graph into node clusters in an iterative manner before some stopping criterion is met. Other statistical approaches for community detection often require model choices and prior selection in Bayesian analyses, which are difficult without some amount of data inspection and pre-processing. Because communities are often fuzzily-defined human concepts, an alternative approach is to leverage human vision to identify communities. The work presents a tool for community detection in form of a web application, called gravicom, which facilitates the detection of community structures through visualization and direct user interaction. In the process of detecting communities, the gravicom application can serve as a standalone tool or as a step to potentially initialize (and/or post-process) another community detection algorithm. In this paper we discuss the design of gravicom and demonstrate its use for community detection with several network data sets. An "Appendix" describes details in the technical formulation of this web application built on the R package Shiny and the JavaScript library D3.

Investors' Country Risk Appetite: A Complex System Perspective Involving the External Debt Composition

Since the outbreak of the financial crisis in mid-2007, the perspective that the structure of the markets influences the nature of the crisis has become widely accepted. I introduce a complex system approach to investigate how financial distress may have been imported by low and middle income countries through the external debt compositions, hence suggesting to exploit the sovereign debt structures to implement network formation. This stimulates to detect not only how the external debt composition of a single country affects its economic performance, but also how the overall structure of the system may contribute to explain its economic results. In particular, I apply a community detection methodology to study the potential relationships between economic performances and political institutions. Results underline that the structure of the system has been influenced by the onset of the crisis, while the impact on investors' country risk appetite is still up for discussion.