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The investigation of community structures in complex networks is an important issue in many domains and disciplines. In this paper, we propose a novel method to address the problem based on evaluation of the community structure. By testing the proposed algorithm on artificial and real-world networks, experimental results demonstrate that our approach is both accurate and fast. Our algorithm may shed light on uncovering the universal principles of network architectures and topologies.

We propose quantitative definitions of network community and hierarchy in collaboration networks described by bipartite graphs, whose basic elements, named actors, take part in events, organizations or activities, named acts. We show, by examples, in some practical collaboration networks that the network acts, represented by certain complete subgraphs in the projected single-mode network, usually are highly interwoven. However, we can easily identify the communities by the definition and the algorithm presented in this paper. We also propose a novel statistical quantity, i.e., interweavement, which describes quantitatively how the acts are interwoven in a collaboration network.

In this paper, we propose a new algorithm that minimizes the worst expected growth of an epidemic by reducing the size of the largest connected component (LCC) of the underlying contact network. The proposed algorithm is applicable to any level of available resources and, despite the greedy approaches of most immunization strategies, selects nodes simultaneously. In each iteration, the proposed method partitions the LCC into two groups. These are the best candidates for communities in that component, and the available resources are sufficient to separate them. Using Laplacian spectral partitioning, the proposed method performs community detection inference with a time complexity that rivals that of the best previous methods. Experiments show that our method outperforms targeted immunization approaches in both real and synthetic networks.

Link prediction is a vital aspect of analyzing network evolution and identifying potential connections in complex networks. Previous studies have primarily focused on the information of common neighbors between nodes, often overlooking the inherent attributes of nodes. This study proposes community-based popularity, an attribute of nodes that considers changes in the neighborhood over time in conjunction with the community structure. Based on this attribute, we improve similarity-based link prediction methods. The experiments utilized unweighted directed networks from three distinct types of trade to evaluate the effectiveness of the improved link prediction methods. The training and probe sets were divided in chronological order. The experimental results show that the improved methods provide better link prediction results than the compared methods.

How to identify community structure in complex network is of theoretical significance, which relates to help to analyze the network topology and understand the network works. Determining the optimal number of communities is a nontrivial problem in detecting community structure. In this paper, we propose a novel method for detecting the optimal number of communities. Based on the local random walk (LRW) measurement, the distance index between each pair of nodes of a network is calculated firstly. Then the optimal number of communities can be found based on the idea that community centers are characterized by a higher density than their neighbors and by a relatively large distance from nodes with higher densities. The experimental results show that the method is effective and efficient in both artificial and real-world networks.

As we know, the scale-free property of networks implies that there are a great deal of nodes with low degree and a few with high degree in networks. In this paper, we mainly stick to the analysis of the heterogeneity of social networks. Heterogeneity measure of degree sequence based on Laplacian centrality (HLC) and degree ratio (HDR) and local neighborhood (HLN) are presented. Furthermore, heterogeneities of community based on the size, edge abundant degree and density of community are explored, respectively. The heterogeneity measures of community are empirically analyzed in the real-world network in terms of these three indices, i.e., size, edge abundant degree and density of community.

The simple efficiency model is developed on scale-free networks with communities to study the effect of the communities in complex networks on efficiency dynamics. For some parameters, we found that the state of system will transit from a stagnant phase to a growing phase as the strength of community decreases.

Identifying community structures in bipartite networks is a popular topic. People usually focus on one of two modes in bipartite networks when uncovering their community structures. According to this understanding, we design a community detection algorithm based on preferred mode in bipartite networks. This algorithm can select corresponding preferred mode according to specific application scenario and effectively extract community information in bipartite networks. The trials in artificial and real-world networks show that the algorithm based on preferred mode has better performances in both small size of bipartite networks and large size of bipartite networks.

ASEAN Economic Community 2015: Opportunities for Trade and Investment in Thailand's Biotechnology Sector.

Advancing Thailand's Rice Agriculture through Molecular Breeding.

Boosting Thailand's Food and Feed Industry: The Food and Feed Innovation Center.

Building Manpower Capacity and Capabilities for Thailand and ASEAN.

Pursuing Immunopathophysiology Research to Support Typhus Vaccine Developement.

Hospitals' Community Benefits.

The Question of Fair Benefits in International Research.

Access to Medicines and Corporate Social Responsibilities of the Pharmaceutical Industry.

Corporate Social Responsibility of the Pharmaceutical Industry in Solidaristic Terms.

A matter of the heart.

Discovering opportunities in China’s cardiovascular market.

Improving outcomes and expanding indications with Transcatheter Aortic Valve Implantation (TAVI).

Community-based cardiac rehabilitation in Singapore.

The gift of life: 50 years of human heart transplant.

Objectives: To understand the social network characteristics and network satisfaction of people with psychiatric disabilities living in the community in Hong Kong. Method: A modified scale was administered to 251 service users attending 5 Community Mental Health Link (LINK) Services measuring the above variables. Findings: The study suggested that people with psychiatric disabilities living in the community have a small social network. Moreover, network satisfaction is related to network size and physical proximity. The results are discussed in terms of their consistency with overseas findings and the implications for social work practice in network building for people with psychiatric disabilities. Conclusion: The results provided an initial understanding of social network characteristics and network satisfaction of service users receiving LINK service, with particular reference to network size and physical proximity.

目的：探討本地精神病康復者的社交網絡特質及他們對社交網絡的滿意程度。方法： 共251名來自5個「精神健康連網」的服務使用者填寫一份問卷，量度他們的社交網絡特質及對社交網絡的滿意程度。結果： 在社區生活的精神病康復者的社交網絡較小，而他們對社交網絡的滿意程度與其網絡的大小及地域的距離有明顯的關係。研究結果與海外的研究結果一致，爲社會工作實務上提供初步的討論。結論： 研究結果爲探討精神病康復者社交網絡的特質及他們對社交網絡的滿意程度提供初步的了解，更爲研究網絡的大小及地域的距離提供參考。.

Real-world relations are often represented as bipartite networks, such as paper-author networks and event-attendee networks. Extracting dense subnetworks (communities) from bipartite networks and evaluating their qualities are practically important research topics. As the attempts for evaluating divisions of bipartite networks, Guimera and Barber propose bipartite modularities. This paper discusses the properties of these bipartite modularities and proposes another bipartite modularity that allows one-to-many correspondence of communities of different vertex types.

Goal/purpose: This study focused on information professionals working in the GLAM (galleries, libraries, archives and museums) sector, and how information was sought and used by them for community engagement and to attain wiser outcomes. The primary purpose was to investigate the information collection, use, reflection and values of professionals in the GLAM sector to determine if wise actions occur that may potentially benefit the community.

Methodology: A qualitative approach was used to conduct this research using the wise action model’s (WAM) wisdom characteristics. Data were collected from information professionals working in managerial positions in the GLAM sector using in-depth interviews. Thematic analysis was used to analyse the data.

Results: The findings indicate that while most participants exhibit some elements of wisdom, there are gaps that need to be addressed before wise functioning is deemed applicable in their roles. While knowledgeable information acquisition and community engagement were very visible, more emphasis on values and stakeholder well-being is recommended for wiser considerations.

Originality/Value: Study of wisdom certainly deserves more attention in knowledge management research as previous studies have indicated. With increasing stresses in the lives of professionals, it is now more important than ever to gain an understanding of how much wisdom prevails in organisational functioning to improve the works of individuals and consequently improve the well-being of impacted communities.

This paper focuses on a design of improved framework and analysis of existing framework which exploits certain algorithms for tracking online community in social network. Tracking of online community is an imperative task where the goal is to identify meaningful group structures in the dynamic social network and consider the problem of the evolution of groups of users in dynamic scenarios. Existing frameworks for tracking community in social network have some limitation which makes it less scalable and computationally inefficient. This novel framework facilitates scalable tracking communities over the time in social networks and offers efficient methods to deal with the problems which are offered in most of the existing frameworks.

This study examines the causes and dynamics in the creation of business ventures by minority nascent entrepreneurs. Minority business enterprises are an important source of job creation and innovation in the US economy, as well as economic development engines in their respective communities. However, little is understood about the unique motivations, business strategies and plans in the early stage of their venture formation. This paper utilizes the Panel Study of Entrepreneurial Dynamics (PSED) dataset in investigating black and Hispanic entrepreneurial entry as compared to white nascent entrepreneurs around three important dimensions: motivation, business strategy, and community resources. It is found that blacks are highly driven by a range of motivational factors while Hispanics value intergenerational inheritance and role models in business ownership. Both groups heavily focus on a niche market strategy by lowering prices, serving markets missed by others and locating close to customers. Contrary to expectation, their perceptions of community resources are not more favorable than whites. Public policy implications are discussed.

Scientific communities are bound together by common purpose and interests, and tangible evidence of the structure of such communities may be found by investigating co-authorship networks. We utilise social network analysis to examine the network structure of International Society for Professional Innovation Management (ISPIM), using co-authorship data from six ISPIM events during the years 2009–2011. We find interesting evidence of the network structure, illustrating vividly the central authors and sub-components of the network. Related to this, results reveal surprisingly tight clustering based on geographical and institutional boundaries. We also find evidence of high performing authors which span these boundaries via significantly different strategies. Overall, the results help to uncover the underlying structure of the scholarly network behind ISPIM, which helps to better understand the key contributors and their networks, and also the development points and promising research collaboration opportunities.

This paper explores how social impact assessments (SIAs) can be applied productively in mining development in African societies characterised by weak regulatory structures and ethnic diversity. It evaluates the nature of the social risks associated with mining development in Africa and examines the concept of SIA as a tool for mitigating those risks. Most importantly, it considers the factors that should be taken into account in the design and implementation of SIA programs in ethnically diverse African regions. One of the paper's central theses is that within-group perceptions of procedural justice are instrumental to the success of participatory processes for SIAs in mining development in Africa.

Mine closure is an integral part of the lifecycle of a mining project. The closure of a mine has social impacts on the surrounding community and employees who have gradually become dependent on the mine financially, culturally and emotionally. By recognising the consequences of mine closure on local communities, companies respond to the assessment and management of the social impacts. While there are applications of social impact assessments (SIAs) for areas available in different sectors, there are limited practices of SIA for mine closure planning, and there is a lack of information on the role of SIA in mine closure planning. Importantly, there is a dearth of information on how SIA can play an important role to make a mine closure plan by bringing all stakeholders together. This empirical study investigated the contribution of SIA on the development of a coal mine closure plan in regional Queensland in Australia. By integrating social issues and community concerns into the closure planning process, through SIA of the closure planning, the mining company, its employee and the local community were collectively able to formulate the mine closure. This study shows how the SIA can be used to bring relevant stakeholders together to formulate the plan for mine closure and make it acceptable to the stakeholders including company, local communities and employees. Practical policy implications include community engagement through SIA and an assurance of the socioeconomic security of the local community and employees of the mine. It is crucial to undertake SIA at the beginning of the closure planning process and involve the relevant stakeholders to formulate closure plan acceptable to all relevant parties. For the development of a mine closure plan, particular attention is required to address the community’s concerns and the development of a solid relationship with the community through negotiations. It is expected that the findings of this study will be useful to researchers, practitioners and other interested persons, not only in Australia but also in other countries with a similar context.

There is a growing need for integrated approaches that align community priorities with strategies that build resilience to climate hazards, societal shocks, and economic crises to ensure more equitable and sustainable outcomes. We anticipate that adaptive management and resilience learning are central elements for these approaches. In this paper, we describe an approach to build and test a Resilience Learning System to support research and implementation of a resilience strategy developed for the Greater Miami and the Beaches or the Resilient305 Strategy. Elements foundational to the design of this integrated research strategy and replicable Resilience Learning System are: (1) strong partnerships among community members, government and non-government organization leaders, and researchers from multiple academic institutions; (2) contributions of subject matter expertise and local knowledge to identify information and translational gaps, formulate metrics and evaluate outcomes of Resilient305 Strategy actions from the community perspective; and (3) a comprehensive understanding of civic engagement activities, technological tools, and resilience-building capacities, including policy and financial innovations, from which to advance socio-technological, smart and connected regional-to-hyperlocal community translation through co-design/co-production. Initial results on co-produced metrics are provided. This work produces a new, replicable framework for resilience research that includes a comprehensive set of metrics, translation to communities through structured dialogues, a collaborative process involving all stakeholders and researchers, and evaluation of resilience actions to inform new investments and improve understanding and effectiveness over time.