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With the rapid development of internet technology, open online social networks provide a broader platform for information spreading. While dissemination of information provides convenience for life, it also brings many problems such as security risks and public opinion orientation. Various negative, malicious and false information spread across regions, which seriously affect social harmony and national security. Therefore, this paper aims to minimize negative information such as online rumors that has attracted extensive attention. The most existing algorithms for blocking rumors have prevented the spread of rumors to some extent, but these algorithms are designed based on entire social networks, mainly focusing on the microstructure of the network, i.e. the pairwise relationship or similarity between nodes. The blocking effect of these algorithms may be unsatisfactory in some networks because of the sparse data in the microstructure.

An algorithm for minimizing the influence of dynamic rumor based on community structure is proposed in this paper. The algorithm first divides the network into communities, and integrates the influence of each node within communities and rumor influence probability to measure the influence of each node in the entire network, and then selects key nodes and bridge nodes in communities as blocked nodes. After that, a dynamic blocking strategy is adopted to improve the blocking effect of rumors.

Community structure is one of the most prominent features of networks. It reveals the organizational structure and functional components of a network from a mesoscopic level. The utilization of community structure can provide effective and rich information to solve the problem of data sparsity in the microstructure, thus effectively improve the blocking effect. Extensive experiments on two real-world data sets have validated that the proposed algorithm has superior performance than the baseline algorithms.

As an important research direction of social network analysis, rumor minimization has a profound effect on the harmony and stability of society and the development of social media. However, because the rumor spread has the characteristics of multiple propagation paths, fast propagation speed, wide propagation area and time-varying, it is a huge challenge to improve the effectiveness of the rumor blocking algorithm.

This paper aims that privacy research is divided in distinct communities and rarely considered as a singular field, harming its disciplinary identity. The authors collected 119.810 publications and over 3 million references to perform a bibliometric domain analysis as a quantitative approach to uncover the structures within the privacy research field.

The bibliometric domain analysis consists of a combined directed network and topic model of published privacy research. The network contains 83,159 publications and 462,633 internal references. A Latent Dirichlet allocation (LDA) topic model from the same dataset offers an additional lens on structure by classifying each publication on 36 topics with the network data. The combined outcomes of these methods are used to investigate the structural position and topical make-up of the privacy research communities.

The authors identified the research communities as well as categorised their structural positioning. Four communities form the core of privacy research: individual privacy and law, cloud computing, location data and privacy-preserving data publishing. The latter is a macro-community of data mining, anonymity metrics and differential privacy. Surrounding the core are applied communities. Further removed are communities with little influence, most notably the medical communities that make up 14.4% of the network. The topic model shows system design as a potentially latent community. Noteworthy is the absence of a centralised body of knowledge on organisational privacy management.

This is the first in-depth, quantitative mapping study of all privacy research.

This study aims to identify the gaps in current policy and propose a viable framework for policy improvement regarding people-centered tsunami early warning chain in Padang City. The objectives are: to describe the gaps and flaws in the current policy regarding local tsunami early warning chain, to identify potential actors to be involved in the tsunami early warning chain and to assess the roles and capacity of actors, and their potential for involvement in early warning.

This study is an exploratory study using social network analysis (SNA) on regulations and other legal documents, and primary data sources from a focus group discussion and semi-structured interviews.

The study found that the existed regulation lacks extension nodes to relay warnings to the populations at risk, often referred to as “the last mile.” Moreover, receiving warning information from both formal and informal sources is important to mobilize people evacuation more effectively during an emergency. The study found that mosque communities and disaster preparedness leaders are the potential actors who should be involved in the local early warning chain.

The research findings were presented as a recommendation to Padang City Government and have been legalized as the new tsunami early warning chain procedure in the Padang City Mayor Regulation 19/2018.

This research investigated local tsunami early warning dissemination in Padang City using SNA. The study demonstrates a close collaboration between researchers, practitioners and the community.

In this article we aim to understand how the network formed by fitness tracking devices and associated apps as a subset of the broader health-related Internet of things is capable of spreading information.

The authors used a combination of a content analysis, network analysis, community detection and simulation. A sample of 922 health-related apps (including manufacturers' apps and developers) were collected through snowball sampling after an initial content analysis from a Google search for fitness tracking devices.

The network of fitness apps is disassortative with high-degree nodes connecting to low-degree nodes, follow a power-law degree distribution and present with low community structure. Information spreads faster through the network than an artificial small-world network and fastest when nodes with high degree centrality are the seeds.

This capability to spread information holds implications for both intended and unintended data sharing.

The analysis confirms and supports evidence of widespread mobility of data between fitness and health apps that were initially reported in earlier work and in addition provides evidence for the dynamic diffusion capability of the network based on its structure. The structure of the network enables the duality of the purpose of data sharing.

The rate of urbanisation in China has accelerated community heterogeneity, and yet it has also led to challenges and problems in community governance. This trend has been accompanied by the rapid expansion of information and communication technology (ICT) and online activities. Based on the example of Jiangqiao Township in Shanghai, this paper aims to probe the link between online participation using the internet and its impacts on social capital formation and community development.

A literature review was conducted, and a case study method based on quantitative data was applied to test the theoretical framework in the interactions of users’ online participation and perceptions of community governance.

Participation in an online community through the internet was found to foster new social capital. Distributed social capital had a positive impact on perceptions of governance at the community level, which was due to the resulting network density and social trust of the locality.

This study offers an expanded perspective on the impact of the internet on the behaviour of netizens in China in the context of community governance in new settlements and townships. During the COVID-19 pandemic, it is interesting to investigate how the use of mass communication channels, such as the internet and other digital platforms, affects social behaviour and generates new social norms. This study offers quantitative evidence from China to support the theory of Putnam (1993; 1995a). It thus extends beyond the field of sociology to the fields of public administration and urban development.

The purpose of this paper is to develop a more detailed understanding of how embedding in different social networks relates to different types of action that individuals choose in the context of organizational closures, downsizing or relocations. To develop such insights, this paper focuses on three particular types of social networks, namely, intra-organizational; external professional and local community networks. These three types of networks have been frequently related to different types of action in the context of closures and relocations.

This is a conceptual paper. The authors develop the argument by integrating relevant recent literature on the salience related to embedding in different types of social networks, with a particular focus on responses to organizational closure or relocation.

The authors argue that at times of industrial decline and closure: embeddedness in intra-organizational networks can favor collective direct action; embeddedness in professional networks is likely to favor individual direct action and embeddedness in community networks can lead to individual indirect action. The authors then add nuance to the argument by considering a range of complicating factors that can constrain or enable the course (s) of action favored by particular combinations of network influences.

On a theoretical level, this paper adds to understandings of the role of network embeddedness in influencing individual and collective responses to such disruptive events; and direct or indirect forms of response. On a practical level, the authors contribute to understandings about how the employment landscape may evolve in regions affected by organizational demise, and how policymakers may study with or through network influences to develop more responsible downsizing approaches.

Nowadays, networks are the basic core of social organization and interaction; consequently, networking has become an indispensable requirement in today's societies. In education, the development of different types of collaborative networks has been observed, leading, in recent years, to the creation of educational macro-networks that generate links between schools and different types of institutions. Therefore, this study consists of a systematic review to examine the most relevant findings on this type of educational macro-networks. The authors’ objectives were: to examine the main characteristics of this type of networks; to analyze the consequences they cause; and to determine which aspects contribute to or hinder their construction and development.

A bibliographic search was carried out in Web of Science, SCOPUS and Education Resources Information Center, which yielded a total of 29 studies that were analyzed thematically using Nvivo software.

Among the most noteworthy results are: the wide variety of existing structures; the benefits they generate in terms of learning outcomes, educational equity and the reduction of inequalities; and the influence exerted by the type of leadership and policies in the development of this macro-networks. Finally, future lines of research and policy implications for practice are highlighted.

In this society, networks have become the basic core of an organization and the new form of social interaction. As a result, networks have begun to play a fundamental role in today's societies, even permeating various spheres such as education. In this context, it is essential to analyse this type of relationship in the field of education in order to truly understand its implications in this sector as well as its development and structure.

Current research on millennials primarily focuses on their behavior within an academic or workplace setting. This study expands on previous analysis by exploring how millennials respond to community leadership efforts, particularly cohort leadership programs. Participant outcomes from University of Minnesota Extension’s County Bridging Leadership Program revealed that millennials—particularly those without a four-year degree—experienced significantly higher gains in several skill areas relevant to community development than non- millennials. Recruiting more millennials to participate in community leadership programs is critical not only to keep younger people in rural communities but also to strengthen future community vitality.

This study aims to assess the effect of updating the Phase I data – to enhance the parameters' estimates – on the control charts' detection power designed to monitor social networks.

A dynamic version of the degree corrected stochastic block model (DCSBM) is used to model the network. Both the Shewhart and exponentially weighted moving average (EWMA) control charts are used to monitor the model parameters. A performance comparison is conducted for each chart when designed using both fixed and moving windows of networks.

Our results show that continuously updating the parameters' estimates during the monitoring phase delays the Shewhart chart's detection of networks' anomalies; as compared to the fixed window approach. While the EWMA chart performance is either indifferent or worse, based on the updating technique, as compared to the fixed window approach. Generally, the EWMA chart performs uniformly better than the Shewhart chart for all shift sizes. We recommend the use of the EWMA chart when monitoring networks modeled with the DCSBM, with sufficiently small to moderate fixed window size to estimate the unknown model parameters.

This study shows that the excessive recommendations in literature regarding the continuous updating of Phase I data during the monitoring phase to enhance the control chart performance cannot generally be extended to social network monitoring; especially when using the DCSBM. That is to say, the effect of continuously updating the parameters' estimates highly depends on the nature of the process being monitored.

The emergence of networks within education has been driven by a number of factors, including: the complex nature of the issues facing education, which are typically too great for single schools to tackle by themselves; changes to educational governance structures, which involve the hollowing out of the middle tier and the introduction of new approaches with an individualized focus; in addition is the increased emphasis on education systems that are “self-improving and school-led”. Within this context, the realization of teacher and school improvement actively emerges from establishing cultures of enquiry and learning, both within and across schools. Since not every teacher in a school can collaboratively learn with every other teacher in a network, the most efficient formation of networks will comprise small numbers of teachers learning on behalf of others.

Within this context, Professional Learning Networks (PLNs) are defined as any group who engage in collaborative learning with others outside of their everyday community of practice; with the ultimate aim of PLN activity being to improve outcomes for children. Research suggests that the use of PLNs can be effective in supporting school improvement. In addition, PLNs are an effective way to enable schools to collaborate to improve educational provision in disadvantaged areas. Nonetheless harnessing the benefits of PLNs is not without challenge. In response, this paper explores the notion of PLNs in detail; it also sheds light on the key factors and conditions that need to be present if PLNs are to lead to sustained improvements in teaching and learning. In particular, the paper explores the role of school leaders in creating meaningful two-way links between PLNs and their schools, in order to ensure that both teachers and students benefit from the collaborative learning activity that PLNs foster. The paper concludes by suggesting possible future research in this area.