A label-switching algorithm for fast core-periphery identification

Structural correlation between communities and core-periphery structures in social networks: Evidence from Twitter data

The emergence and growth in social networking data, where the information gets shared without veracity, results in the problem of rumor dissemination. The negative effects of rumor diffusion can be controlled by recognizing the origin of a rumor in the network. This paper centers the issue of identifying the origin of a rumor in the social network by reducing the search space. In the previous work, they followed monitor-based observation for small networks or with multiple snapshots for large networks, which shows good accuracy but required more time. The proposed model mainly focused on reducing the search space by identifying the candidate partition, where the source node is present. We follow the discrete-time susceptible-infected model and monitor-based approach for the assessment of the origin. We have proposed a method for progressive delay during the process of diffusion. The origin has been determined by applying the maximum likelihood estimator on the candidate partition. The experiment has been performed in a synthetic and real-world network. The experimental result shows that the real source is identified within 0–2 hops distance in a synthetic network and within 0–4 hops in a real-world network.

Automatic fingerprint identification systems use level-1 and level-2 features for fingerprint identification. However, forensic examiners utilize inherent level-3 details along with level-2 features. Existing level-3 feature extraction algorithms are computationally expensive to be used for identification. This paper presents a novel algorithm for fast level-3 feature extraction and identification. The algorithm starts with computing local image quality score using redundant discrete wavelet transform. A fast curve evolution algorithm is then used to extract four level-3 features namely, pores, ridge contours, dots, and incipient ridges. Along with level-1 and level-2 features, these level-3 features are used in a Delaunay triangulation based indexing algorithm. Finally, quality-based likelihood ratio is used to further improve the identification performance. Experiments conducted on a high resolution fingerprint database containing rolled, slap and latent images indicate that the algorithm offers significant benefits for fast fingerprint identification.

A novel approach for analyzing the structural integrity and operational vulnerability of complex networks using intuitionistic fuzzy graphs has been modeled. While traditional fuzzy graph metrics focus primarily on existence, they fail to capture the holistic systemic impact of failures. To overcome this limitation, a scalar-based measure of nodal importance that integrates both existence (membership degree) and non-existence (non-membership degree) values of incident edges into a single critical metric has been developed. The proposed indices demonstrate enhanced sensitivity to network perturbations compared to conventional degree centrality measures, capturing latent vulnerabilities in critical infrastructure topologies. Based on this, two indices are proposed: Intuitionistic Fuzzy Degree Index and Intuitionistic Edge Interaction Index. These indices quantify the total system activity, stress dispersion, overall network cohesiveness, and potential for cascading failure propagation. When applied to synthetic core-periphery networks, the proposed indices identified critical nodes with superior discrimination capability compared to existing fuzzy graph metrics, revealing that removal of identified nodes results in system-wide connectivity degradation observable through both membership and non-membership approximations. This methodology was applied to a core-periphery communication network to analyze the systemic consequences of node removal. Experimental validation on networks of varying sizes demonstrates that the Intuitionistic Edge Interaction Index achieves robust node criticality ranking across heterogeneous network topologies with improved predictive accuracy for cascade initiation points. This work provides network analysts and engineers a quantitative tool to precisely assess criticality and inform targeted resilience strategies in uncertain, high-risk environments.

Core–periphery detection aims to separate the nodes of a complex network into two subsets: a core that is densely connected to the entire network and a periphery that is densely connected to the core but sparsely connected internally. The definition of core–periphery structure in multiplex networks that record different types of interactions between the same set of nodes on different layers is non-trivial since a node may belong to the core in some layers and to the periphery in others. We propose a nonlinear spectral method for multiplex networks that simultaneously optimizes a node and a layer coreness vector by maximizing a suitable non-convex homogeneous objective function by a provably convergent alternating fixed-point iteration. We derive a quantitative measure for the quality of a given multiplex core–periphery structure that allows the determination of the optimal core size. Numerical experiments on synthetic and real-world networks illustrate that our approach is robust against noisy layers and significantly outperforms baseline methods while improving the latter with our novel optimized layer coreness weights. As the runtime of our method depends linearly on the number of edges of the network, it is scalable to large-scale multiplex networks.

Purpose This study aims to analyze a trade network between countries on agricultural products exports and imports from a social network perspective, and specifically identify the core and peripheral countries of agricultural product trade network. Design/Methodology/Approach We calculate coreness score and core-peripheral matrix values using core-periphery structure analysis of social network analysis techniques and recommend core countries and periphery countries. Findings We identified countries belonging to each block of the core-periphery structure, namely core block, peripheral block, core-peripheral block, and peripheral-core block, to discover the path and impact of crisis movement. Research Implications This study made it possible to predict how to respond to a crisis in the trade network from a macroscopic point of view to managers of a country or a company. The research implications of this study increased the understanding of core-peripheral structure analysis through network visualization as well as mathematical calculations.

Cooperation coevolution with fast interdependency identification for large scale optimization

This study relates the local property of node dominance to local and global properties of a network. Iterative removal of dominated nodes yields a distributed algorithm for computing a core-periphery decomposition of a social network, where nodes in the network core are seen to be essential in terms of network flow and global structure. Additionally, the connected components in the periphery give information about the community structure of the network, aiding in community detection. A number of explicit results are derived, relating the core and periphery to network flow, community structure, and global network structure, which are corroborated by observational results. The method is illustrated using a real world network (DBLP co-authorship network), with ground-truth communities.

An algorithm for fast extraction and identification of star target

The school is part of our society, which increasingly uses more and more the social networks. Therefore, we must continue this momentum, not only by using them, but by also guiding the students of its proper use. Thus, most educators working with high and middle level schools are aware of the involvement of young people in social networks, but few of these educators are prepared to deal with them the issue. Experts discuss the risks and benefits of such sites, and the role of schools, to offer a comprehensive approach that addresses the needs of online students. This paper seeks a solution to the complex world of social networking in education, where users can belong to a social network not as such, but as a set of objects that define a necessity. This research work tries to redefine social networking of an online community, where objects that work on the same stage define the team. To do this we propose a school architecture, where objects define the curricular activities, with known characteristics.KeywordsEducative systemssocial networkscloud computingWeb Servicessystems architectureStudents CurriculumEducative Curricula

The interaction patterns of employees in social and professional networks play an important role in the success of employees and organizations as a whole. However, in many fields there is a severe under-representation of minority groups; moreover, minority individuals may be segregated from the rest of the network or isolated from one another. While the problem of increasing the representation of minority groups in various fields has been well-studied, diversification in terms of numbers alone may not be sufficient: social relationships should also be considered. In this work, we consider the problem of assigning a set of employment candidates to positions in a social network so that diversity and overall fitness are maximized, and propose Fair Employee Assignment (FairEA), a novel algorithm for finding such a matching. The output from FairEA can be used as a benchmark by organizations wishing to evaluate their hiring and assignment practices. On real and synthetic networks, we demonstrate that FairEA does well at finding high-fitness, high-diversity matchings.

This study uses social network analysis and modified gravity model methods to empirically analyse the network spatial correlation structure and spatiotemporal development trend of 21 cities in Guangdong Province from 2000 to 2020 based on tourism economic development data. The findings show that, first, Zhuhai has the greatest potential for growth as the centre of the spatial and temporal evolution trend of the network structure of the tourism economy in Guangdong Province, ahead of Shenzhen, Huizhou, Zhaoqing, Zhongshan, Jiangmen and Dongguan. However, Guangzhou, the capital city of Guangdong Province, is experiencing a decline in such influence and development. Second, there is a counter-trend growth in the number of tourism-related economic links among the 21 cities. Although Guangdong's tourism economic network intensity is strong, there is still room for further optimisation. Third, the results of the overall network indicators show that there is a need for further improvement in network density, grade and efficiency to help reduce the relative development gap of the cities' tourism and effectively improve the overall development of Guangdong's tourism economy. Finally, based on the core–periphery structure, this study proposes relevant suggestions for the sustainable development of Guangdong's tourism industry.

These proceedings contain the papers selected for presentation at the seventh edition of the ACM SIGSPATIAL Workshop on Location-Based Social Networks (LBSN 2014) which is being held in conjunction with the ACM SIGSPATIAL GIS conference in 2014. Social networks have been prevalent on the Internet and become a hot research topic attracting many professionals from a variety of fields. The advances in location-acquisition and mobile communication technologies empower people to use location data with existing online social networks. The dimension of location helps bridge the gap between the physical world and online social networking services. Furthermore, people in an existing social network can expand their social structure with the new interdependency derived from their locations. As location is one of the most important components of user context, extensive knowledge about an individual's interests, behaviors, and relationships with others can be learned from locations. These kinds of location-embedded and location-driven social structures are known as location-based social networks. The objective of this workshop is to provide professionals, researchers, and technologists with a single forum where they can discuss and share the state-of-the-art of LBSN development and applications, present their ideas and contributions, and set future directions in emerging innovative research for location based social networks. This year program is composed of two tracks of predominantly "social" and "spatial" views on LBSNs.

The purpose of this article is to investigate the information exchange between local and foreign organizations in internationalization projects. This article applies social network analysis to visualize and analyze information exchange networks in internationalization projects. The findings indicate that information exchange networks in internationalization projects follow the core–periphery structure, so a fully connected network with the core offering trust and a loosely connected network with the periphery offering innovation can coexist in an information exchange network. Existing studies have found that a fully connected network used for information exchange can help build up trust, whereas a loosely connected network can bring innovation to a project. On the whole, this study suggests that a fully connected network and a loosely connected network can coexist in a core–periphery structure.

Social networks currently serve as a primary platform for numerous connected intelligent individuals to share opinions and forward messages, which not only accelerate the diffusion of trending useful information but also facilitate the spreading of rumors and the execution of malicious attacks. Therefore, it is of crucial importance to understand how information diffuses in social networks. Most existing works either adopt empirical machine learning methods, which heavily rely on the quality and quantity of datasets, or develop mathematical dynamic models, which often neglect micro-interactions among users and the effects of time delays caused by users receiving information. In this paper, we propose an explainable framework to analyze information diffusion in social networks, which leverages the graphical evolutionary game theory to model scenarios where users receive and process information at varying rates. We define three types of time delay, model the micro-interaction among users, derive the dynamics of information diffusion, and analyze the conditions of system stability. We run simulations over both synthetic networks and three real-world social networks to validate the correctness of the proposed model. This work can provide a new perspective of modeling and predicting the information diffusion processes with time delays in different scenarios.