Community Detection Techniques Research Articles

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.

From Research to Applications: What Can We Extract with Social Media Sensing?

With the constant growth of social media in our daily lives, a huge amount of information is generated online by multiple social networks. However, what can we actually extract with the science of social media sensing? It is a very challenging task to mine meaningful data out of this vast crowdsourcing volume, which also rapidly changes or ends up being misleading. The scope of this paper is to present different approaches that overcome these challenges and utilize social media information from various sources. This work illustrates applications that: improve the performance of architectural design; preserve the cultural heritage; enhance citizen security; provide early detection for disasters; and discover creeping crisis events. A large variety of analyses are presented, including, among other, disaster or crime event detection, user identity linkage, relevance classification, and community detection techniques. The evaluation of the presented methods is also given in this article, proving that they can be practical and valuable in many applications.

Survey on graph neural network-based community detection and its applications

Detecting communities within a network is a critical component of network analysis. The process involves identifying clusters of nodes that exhibit greater similarity to each other compared to other nodes in the network. In the context of Complex networks (CN), community detection becomes even more important as these clusters provide relevant information of interest. Traditional mathematical and clustering methods have limitations in terms of data visualization and high-dimensional information extraction. To address these challenges, graph neural network learning methods have gained popularity in community detection, as they are capable of handling complex structures and multi-dimensional data. Developing a framework for community detection in complex networks using graph neural network learning is a challenging and ongoing research objective. Therefore, it is essential for researchers to conduct a thorough review of community detection techniques that utilize cutting-edge graph neural network learning methods [102], in order to analyze and construct effective detection models. This paper provides a brief overview of graph neural network learning methods based on community detection methods and summarizes datasets, evaluation metrics, applications, and challenges of community detection in complex networks.

Distributed coordinated control of mixed expressway and urban roads: a sensitivity-based approach

The inherent interdependency between mixed expressway and urban road networks calls for an effective coordinated traffic control approach. This study introduces a sensitivity-based distributed method to coordinate ramp metering and traffic signal control. Firstly, the quantitative relationships between network nodes are established through a developed traffic flow model for mixed networks. The network-level sensitivity information is analyzed to assess the influence of controllers on the traffic state. Additionally, a dynamic subproblems decomposition algorithm is proposed, considering the network’s spatial structure and dynamic traffic characteristics. By employing community detection techniques, tightly connected controllers are clustered to enable efficient and adaptive distributed control. The optimization problem is then decomposed into several subproblems, each formulated as a bi-level programming problem within a model predictive control framework. Numerical experiments demonstrate that the proposed strategy enhances the traffic efficiency of mixed networks with a 14.5% reduction in total travel time during peak hours, which shows better performance compared with the other three existing control strategies.

The organization of semantic associations between senses in language

Abstract Distributional semantic representations were used to investigate crossmodal correspondences within language, offering a comprehensive analysis of how sensory experiences interconnect in linguistic constructs. By computing semantic proximity between words from different sensory modalities, a crossmodal semantic network was constructed, providing a general view of crossmodal correspondences in the English language. Community detection techniques were applied to unveil domains of experience where crossmodal correspondences were likely to manifest, while also considering the role of affective dimensions in shaping these domains. The study revealed the existence of an architecture of structured domains of experience in language, whereby crossmodal correspondences are deeply embedded. The present research highlights the roles of emotion and statistical associations in the organization of sensory concepts across modalities in language. The domains identified, including food, the body, the physical world and emotions/values, underscored the intricate interplay between the senses, emotion and semantic patterns. These findings align with the embodied lexicon hypothesis and the semantic coding hypothesis, emphasizing the capacity of language to capture and reflect crossmodal correspondences’ emotional and perceptual subtleties in the form of networks, while also revealing opportunities for further perceptual research on crossmodal correspondences and multisensory integration.

A Comprehensive Survey on Community Detection With Deep Learning.

Detecting a community in a network is a matter of discerning the distinct features and connections of a group of members that are different from those in other communities. The ability to do this is of great significance in network analysis. However, beyond the classic spectral clustering and statistical inference methods, there have been significant developments with deep learning techniques for community detection in recent years--particularly when it comes to handling high-dimensional network data. Hence, a comprehensive review of the latest progress in community detection through deep learning is timely. To frame the survey, we have devised a new taxonomy covering different state-of-the-art methods, including deep learning models based on deep neural networks (DNNs), deep nonnegative matrix factorization, and deep sparse filtering. The main category, i.e., DNNs, is further divided into convolutional networks, graph attention networks, generative adversarial networks, and autoencoders. The popular benchmark datasets, evaluation metrics, and open-source implementations to address experimentation settings are also summarized. This is followed by a discussion on the practical applications of community detection in various domains. The survey concludes with suggestions of challenging topics that would make for fruitful future research directions in this fast-growing deep learning field.

QuCloud+: A Holistic Qubit Mapping Scheme for Single/Multi-programming on 2D/3D NISQ Quantum Computers

Qubit mapping for NISQ superconducting quantum computers is essential to fidelity and resource utilization. The existing qubit mapping schemes meet challenges, e.g., crosstalk, SWAP overheads, diverse device topologies, etc., leading to qubit resource underutilization and low fidelity in computing results. This article introduces QuCloud+, a new qubit mapping scheme that tackles these challenges. QuCloud+ has several new designs. (1) QuCloud+ supports single/multi-programming quantum computing on quantum chips with 2D/3D topology. (2) QuCloud+ partitions physical qubits for concurrent quantum programs with the crosstalk-aware community detection technique and further allocates qubits according to qubit degree, improving fidelity, and resource utilization. (3) QuCloud+ includes an X-SWAP mechanism that avoids SWAPs with high crosstalk errors and enables inter-program SWAPs to reduce the SWAP overheads. (4) QuCloud+ schedules concurrent quantum programs to be mapped and executed based on estimated fidelity for the best practice. Experimental results show that, compared with the existing typical multi-programming study [ 12 ], QuCloud+ achieves up to 9.03% higher fidelity and saves on the required SWAPs during mapping, reducing the number of CNOT gates inserted by 40.92%. Compared with a recent study [ 30 ] that enables post-mapping gate optimizations to further reduce gates, QuCloud+ reduces the post-mapping circuit depth by 21.91% while using a similar number of gates.

On the role of diffusion dynamics on community-aware centrality measures.

Theoretical and empirical studies on diffusion models have revealed their versatile applicability across different fields, spanning from sociology and finance to biology and ecology. The presence of a community structure within real-world networks has a substantial impact on how diffusion processes unfold. Key nodes located both within and between these communities play a crucial role in initiating diffusion, and community-aware centrality measures effectively identify these nodes. While numerous diffusion models have been proposed in literature, very few studies investigate the relationship between the diffusive ability of key nodes selected by community-aware centrality measures, the distinct dynamical conditions of various models, and the diverse network topologies. By conducting a comparative evaluation across four diffusion models, utilizing both synthetic and real-world networks, along with employing two different community detection techniques, our study aims to gain deeper insights into the effectiveness and applicability of the community-aware centrality measures. Results suggest that the diffusive power of the selected nodes is affected by three main factors: the strength of the network's community structure, the internal dynamics of each diffusion model, and the budget availability. Specifically, within the category of simple contagion models, such as SI, SIR, and IC, we observe similar diffusion patterns when the network's community structure strength and budget remain constant. In contrast, the LT model, which falls under the category of complex contagion dynamics, exhibits divergent behavior under the same conditions.

A Comparative Analysis of Community Detection Agglomerative Technique Algorithms and Metrics on Citation Network

Social Network Analysis is a discipline that represents social relationships as a network of nodes and edges. The construction of social network with clusters will contribute in sharing the common characteristics or behaviour of a group. Partitioning the graph into modules is said to be a community. Communities are meant to symbolize actual social groups that share common characteristics. Citation network is one of the social networks with directed graphs where one paper will cite another paper and so on. Citation networks will assist the researcher in choosing research directions and evaluating research impacts. By constructing the citation networks with communities will direct the user to identify the similarity of documents which are interrelated to one or more domains. This paper introduces the agglomerative technique algorithms and metrics to a directed graph which determines the most influential nodes and group of similar nodes. The two stages required to construct the communities are how to generate network with communities and how to quantify the network performance. The strength and a quality of a network is quantified in terms of metrics like modularity, normalized mutual information (NMI), betweenness centrality, and F-Measure. The suitable community detection techniques and metrics for a citation graph were introduced in this paper. In the field of community detection, it is common practice to categorize algorithms according to the mathematical techniques they employ, and then compare them on benchmark graphs featuring a particular type of assortative community structure. The algorithms are applied for a sample citation sub data is extracted from DBLP, ACM, MAG and some additional sources which is taken from and consists of 101 nodes (nc) with 621 edges € and formed 64 communities. The key attributes in dataset are id, title, abstract, references SLM uses local optimisation and scalability to improve community detection in complicated networks. Unlike traditional methods, the proposed LS-SLM algorithm is identified that the modularity is increased by 12.65%, NMI increased by 2.31%, betweenness centrality by 3.18% and F-Score by 4.05%. The SLM algorithm outperforms existing methods in finding significant and well-defined communities, making it a promising community detection breakthrough.

Exploring 3D community inconsistency in human chromosome contact networks

Abstract Researchers have developed chromosome capture methods such as Hi-C to better understand DNA’s 3D folding in nuclei. The Hi-C method captures contact frequencies between DNA segment pairs across the genome. When analyzing Hi-C data sets, it is common to group these pairs using standard bioinformatics methods (e.g. PCA). Other approaches handle Hi-C data as weighted networks, where connected node pairs represent DNA segments in 3D proximity. In this representation, one can leverage community detection techniques developed in complex network theory to group nodes into mesoscale communities containing nodes with similar connection patterns. While there are several successful attempts to analyze Hi-C data in this way, it is common to report and study the most typical community structure. But in reality, there are often several valid candidates. Therefore, depending on algorithm design, different community detection methods focusing on slightly different connectivity features may have differing views on the ideal node groupings. In fact, even the same community detection method may yield different results if using a stochastic algorithm. This ambiguity is fundamental to community detection and shared by most complex networks whenever interactions span all scales in the network. This is known as community inconsistency. This paper explores this inconsistency of 3D communities in Hi-C data for all human chromosomes. We base our analysis on two inconsistency metrics, one local and one global, and quantify the network scales where the community separation is most variable. For example, we find that TADs are less reliable than A/B compartments and that nodes with highly variable node-community memberships are associated with open chromatin. Overall, our study provides a helpful framework for data-driven researchers and increases awareness of some inherent challenges when clustering Hi-C data into 3D communities.

Route 66: Mutations of the internal market explored through the prism of citation networks

Abstract The article rethinks the mutation of the internal market, charting its metamorphosis from a free trade area to a maze of common policies. It examines the case law of the European Court of Justice from a novel, structural perspective which uses community detection techniques to shed new light on this amply theorized process. The analysis reveals an irreversible shift in the method of integration, from a de-regulatory removal of national rules obstructing free movement (liberalization) to a re-regulatory adoption of common rules and standards promoting free movement (harmonization). The shift, which occurred between 2007 and 2010, signals a new rationale of integration and a reprioritization of the European Union’s economic and non-economic objectives. Finally, the article questions whether said shift calls for a new authorization of Europe to regulate.

SCINOBO: a novel system classifying scholarly communication in a dynamically constructed hierarchical Field-of-Science taxonomy.

Classifying scientific publications according to Field-of-Science taxonomies is of crucial importance, powering a wealth of relevant applications including Search Engines, Tools for Scientific Literature, Recommendation Systems, and Science Monitoring. Furthermore, it allows funders, publishers, scholars, companies, and other stakeholders to organize scientific literature more effectively, calculate impact indicators along Science Impact pathways and identify emerging topics that can also facilitate Science, Technology, and Innovation policy-making. As a result, existing classification schemes for scientific publications underpin a large area of research evaluation with several classification schemes currently in use. However, many existing schemes are domain-specific, comprised of few levels of granularity, and require continuous manual work, making it hard to follow the rapidly evolving landscape of science as new research topics emerge. Based on our previous work of scinobo, which incorporates metadata and graph-based publication bibliometric information to assign Field-of-Science fields to scientific publications, we propose a novel hybrid approach by further employing Neural Topic Modeling and Community Detection techniques to dynamically construct a Field-of-Science taxonomy used as the backbone in automatic publication-level Field-of-Science classifiers. Our proposed Field-of-Science taxonomy is based on the OECD fields of research and development (FORD) classification, developed in the framework of the Frascati Manual containing knowledge domains in broad (first level(L1), one-digit) and narrower (second level(L2), two-digit) levels. We create a 3-level hierarchical taxonomy by manually linking Field-of-Science fields of the sciencemetrix Journal classification to the OECD/FORD level-2 fields. To facilitate a more fine-grained analysis, we extend the aforementioned Field-of-Science taxonomy to level-4 and level-5 fields by employing a pipeline of AI techniques. We evaluate the coherence and the coverage of the Field-of-Science fields for the two additional levels based on synthesis scientific publications in two case studies, in the knowledge domains of Energy and Artificial Intelligence. Our results showcase that the proposed automatically generated Field-of-Science taxonomy captures the dynamics of the two research areas encompassing the underlying structure and the emerging scientific developments.

Towards a taxonomy of waste management research: An application of community detection in keyword network

Our global environment is facing significant waste and waste management challenges, despite minor differences in definitions and what constitutes waste. In recent decades, waste has been the subject of a great deal of research since civilized society needs to manage waste efficiently and effectively. As a result of all of these issues, researchers have focused on improving waste management. To do so, a taxonomy based on the research conducted on waste management will help them. Research taxonomies and visualizations help us find the most important areas of research in waste management. There are a few researchers who taxonomize research waste according to their methodology, but there is no quantitative and data-oriented approach. By using community detection and social network analysis, this study aims to quantify and systematize waste area taxonomy.There have been 9095 articles published on waste management since 2000, and these articles were retrieved from the WOS database to create a keyword network made up of 17,808 members (unique keywords) and 79,763 links in this study. To discover the research areas of waste management, the community detection technique was used in the keyword network, and 10 main communities that represent the main research areas of waste management were discovered and named based on waste management characteristics and waste types. These 10 communities include more than 90% of all waste management research.In the next step and to achieve a hierarchical taxonomy of waste management studies, the community detection algorithm was implemented in ten communities and by discovering sub-communities, a waste management taxonomy with 10 main branches and 26 sub-branches was presented.

Time-Series Analysis of Ship Movements Using Community Detection and Functional Data Analysis across the East Coast of the Republic of Korea

Kim, G.; Jeong, M.-H.; Jeon, S.-B., and Khan, M.S., 2023. Time-series analysis of ship movements using community detection and functional data analysis across the east coast of the Republic of Korea. Journal of Coastal Research, 39(2), 360–365. Charlotte (North Carolina), ISSN 0749-0208. Because of the recent climate change caused by air pollution in ports, global interest in marine spatial planning (MSP) has increased to achieve cost-effective navigation, economic development goals, logistics, and energy transportation. Accordingly, research on ship traffic, the main cause of air pollution in ports, is attracting attention. This study aims to investigate the temporal characteristics of the port network on the east coast of the Republic of Korea using the automatic identification system data set. This study used the Louvain model, a community detection technique for complex networks, and functional data analysis (FDA). The results of the Louvain model showed that the port network of the Republic of Korea has a hub-and-spoke structure in which ports that are close to each other are regionally clustered around the main ports in the community. The FDA results showed seasonal changes in port arrival volume and detection of abnormal ports in Korea's east-coast ports. The results of this study are expected to provide a baseline for policy makers to establish an effective MSP for port management, cost-effective route development, and prediction of the number of operations.

CommunityGCN: community detection using node classification with graph convolution network

PurposeA community demonstrates the unique qualities and relationships between its members that distinguish it from other communities within a network. Network analysis relies heavily on community detection. Despite the traditional spectral clustering and statistical inference methods, deep learning techniques for community detection have grown in popularity due to their ease of processing high-dimensional network data. Graph convolutional neural networks (GCNNs) have received much attention recently and have developed into a potential and ubiquitous method for directly detecting communities on graphs. Inspired by the promising results of graph convolutional networks (GCNs) in analyzing graph structure data, a novel community graph convolutional network (CommunityGCN) as a semi-supervised node classification model has been proposed and compared with recent baseline methods graph attention network (GAT), GCN-based technique for unsupervised community detection and Markov random fields combined with graph convolutional network (MRFasGCN).Design/methodology/approachThis work presents the method for identifying communities that combines the notion of node classification via message passing with the architecture of a semi-supervised graph neural network. Six benchmark datasets, namely, Cora, CiteSeer, ACM, Karate, IMDB and Facebook, have been used in the experimentation.FindingsIn the first set of experiments, the scaled normalized average matrix of all neighbor's features including the node itself was obtained, followed by obtaining the weighted average matrix of low-dimensional nodes. In the second set of experiments, the average weighted matrix was forwarded to the GCN with two layers and the activation function for predicting the node class was applied. The results demonstrate that node classification with GCN can improve the performance of identifying communities on graph datasets.Originality/valueThe experiment reveals that the CommunityGCN approach has given better results with accuracy, normalized mutual information, F1 and modularity scores of 91.26, 79.9, 92.58 and 70.5 per cent, respectively, for detecting communities in the graph network, which is much greater than the range of 55.7–87.07 per cent reported in previous literature. Thus, it has been concluded that the GCN with node classification models has improved the accuracy.

Wide-parameter coarse-grained state mechanism analysis in the methane combustion system

Based on the coarse graining idea, network community detection and bifurcation techniques are combined to analyze detailed combustion models.

Dynamic Community Detection for Brain Functional Networks During Music Listening With Block Component Analysis

The human brain can be described as a complex network of functional connections between distinct regions, referred to as the brain functional network. Recent studies show that the functional network is a dynamic process and its community structure evolves with time during continuous task performance. Consequently, it is important for the understanding of the human brain to develop dynamic community detection techniques for such time-varying functional networks. Here, we propose a temporal clustering framework based on a set of network generative models and surprisingly it can be linked to Block Component Analysis to detect and track the latent community structure in dynamic functional networks. Specifically, the temporal dynamic networks are represented within a unified three-way tensor framework for simultaneously capturing multiple types of relationships between a set of entities. The multi-linear rank- (Lr, Lr, 1) block term decomposition (BTD) is adopted to fit the network generative model to directly recover underlying community structures with the specific evolution of time from the temporal networks. We apply the proposed method to the study of the reorganization of the dynamic brain networks from electroencephalography (EEG) data recorded during free music listening. We derive several network structures ( Lr communities in each component) with specific temporal patterns (described by BTD components) significantly modulated by musical features, involving subnetworks of frontoparietal, default mode, and sensory-motor networks. The results show that the brain functional network structures are dynamically reorganized and the derived community structures are temporally modulated by the music features. The proposed generative modeling approach can be an effective tool for describing community structures in brain networks that go beyond static methods and detecting the dynamic reconfiguration of modular connectivity elicited by continuously naturalistic tasks.

Deep Learning in Social Networks for Overlappering Community Detection

The collection of nodes is termed as community in any network system that are tightly associated to the other nodes. In network investigation, identifying the community structure is crucial task, particularly for exposing connections between certain nodes. For community overlapping, network discovery, there are numerous methodologies described in the literature. Numerous scholars have recently focused on network embedding and feature learning techniques for node clustering. These techniques translate the network into a representation space with fewer dimensions. In this paper, a deep neural network-based model for learning graph representation and stacked auto-encoders are given a nonlinear embedding of the original graph to learn the model. In order to extract overlapping communities, an AEOCDSN algorithm is used. The efficiency of the suggested model is examined through experiments on real-world datasets of various sizes and accepted standards. The method outperforms various well-known community detection techniques, according to empirical findings.

A Graph-Based Blocking Approach for Entity Matching Using Contrastively Learned Embeddings

Data integration is considered a crucial task in the entity matching process. In this process, redundant and cunning entries must be identified and eliminated to improve the data quality. To archive this, a comparison between all entities is performed. However, this has quadratic computational complexity. To avoid this, `blocking' limits comparisons to probable matches. This paper presents a k-nearest neighbor graph-based blocking approach utilizing state-of-the-art context-aware sentence embeddings from pre-trained transformers. Our approach maps each database tuple to a node and generates a graph where nodes are connected by edges if they are related. We then invoke unsupervised community detection techniques over this graph and treat blocking as a graph clustering problem. Our work is motivated by the scarcity of training data for entity matching in real-world scenarios and the limited scalability of blocking schemes in the presence of proliferating data. Additionally, we investigate the impact of contrastively trained embeddings on the above system and test its capabilities on four data sets exhibiting more than 6 million comparisons. We show that our block processing times on the target benchmarks vary owing to the efficient data structure of the k-nearest neighbor graph. Our results also show that our method achieves better performance in terms of F1 score when compared to current deep learning-based blocking solutions.

Enhancing topological information of the Lyapunov‐based distributed model predictive control design for large‐scale nonlinear systems

SummaryIn this paper, a distributed model predictive control architecture based on graph theory for integrated large‐scale nonlinear process systems is proposed. This architecture is first agglomerated using popular community detection techniques and then organized on account of the relative master–slave relationship according to the ample information of interactions among separate subsystems. Both sequential and iterative distributed nonlinear model predictive coordination forms are considered for the reduction of the communication and computational burden within an acceptable loss of performance. Furthermore, the control performance of the large‐scale integrated system could be improved to some extent under the architecture and the communication strategy we propose, whereby a brave exploration is made on the relationship between the control structure and the control performance, ulteriorly obtaining some notable results towards the untapped territory. The effectiveness of the proposed coordination method is evaluated by a standard reactor‐separator process system.