Structure Of Collaboration Networks Research Articles

Insights of machine learning-based threat identification schemes in advanced network system

An advanced network system (ANS) is characterized by extensive communication features that can support a sophisticated collaborative network structure. This is essential to hosting various forms of upcoming modernized and innovative applications. Security is one of the rising concerns associated with ANS deployment. It is also noted that machine learning is one of the preferred cost-effective ways to optimize the security strength and address various ongoing security problems in ANS; however, it is still unknown about its overall effectivity scale. Hence, this paper contributes to a systematic review of existing variants of machine learning approaches to deal with threat identification in ANS. As ANS is a generalized form, this discussion considers the impact of existing machine learning approaches on its practical use cases. The paper also contributes towards critical gap analysis and highlights the study's potential learning outcome.

Cross-sector partnerships in the South City Choice Neighborhoods Initiative (CNI): An exploratory case study of collaboration network structure and process in Memphis, Tennessee

ABSTRACT Mixed-income initiatives represent promising anti-poverty strategies that assist families living in public housing in the United States. However, little is known about the extensive cross-sector partnerships required for implementation. We address these knowledge gaps by exploring partnerships within the South City Choice Neighborhoods Initiative (CNI) in Memphis, Tennessee. The following research questions guide the study: (1) what is the structure of inter-organizational relationships within the South City CNI partner network? and (2) what collaboration processes emerge within the network structure regarding communication, collaboration, and trust? Utilizing an exploratory case study design that leveraged social network analysis (SNA) and in-depth interviews, we found that the South City CNI was relatively dense and moderately centralized. A core group of partners was central for coordinating information and resources, but roles and positionality changed across each network. The presence of a high-capacity lead organization and multiple bridging organizations aided implementation. We conclude by discussing implications for practice, policy, and research.

Scientific Collaboration Network Structure and Connectedness Among the “Belt and Road” Countries

Scientific Collaboration Network Structure and Connectedness Among the “Belt and Road” Countries

Evolution of Project-Based Collaborative Networks for Implementing Prefabricated Construction Technology: Case Study in Shanghai

Prefabricated construction (PC) is considered to be a low-carbon construction method. Implementing prefabricated construction projects (PCPs) requires multiple industry organizations to participate and collaborate. As different PCPs are initiated and implemented, industry organizations will gradually gather into a complex and evolving collaborative network at the industry level based on specific collaboration relationships with different project backgrounds. The evolution of the collaborative network is related to how industry organizations interact with each other, and how PC-related knowledge and innovation has spread among organizations in the long term. However, the laws of network evolution and the micro effects that drive network evolution are still unknown. This study analyzes 236 prefabricated construction projects (PCPs) in Shanghai during 2015–2023, using the stochastic actor-oriented models (SAOM) method to explore how the macro structure of project-based inter-organizational collaborative networks for prefabricated construction (PC) technology implementation evolves over time, and how micro effects jointly support the evolution of the networks. The macro-level descriptive analysis of the network indicates that the collaborative network has become increasingly dense over time and continues to show a core–peripheral structure, with a small number of super-connected organizations. The micro-level SAOM analysis further reveals that the evolution of the collaborative network structure is driven by structure-based preferential attachment and geographic proximity effects, as well as attribute-based ownership similarity effect. This exploratory effort applies a network dynamics model to investigate the micro mechanism of the evolution of inter-organizational collaboration. The research results provide theoretical guidance and decision-making references for PC industry organizations to develop efficient network action strategies. In addition, it can help industry managers to formulate appropriate network management strategies.

Longitudinal analysis of interorganizational collaborative networks of cross-laminated timber (CLT) construction

Purpose Cross-laminated timber (CLT) is an innovative construction material that provides a balanced mix of structural stiffness, fabrication flexibility and sustainability. CLT development and innovation diffusion require close collaborations between its supply chain architectural, engineering, construction and manufacturing (AECM) stakeholders. As such, the purpose of this study is to provide a preliminary understanding of the knowledge diffusion and innovation process of CLT construction. Design/methodology/approach The study implemented a longitudinal social network analysis of the AECM companies involved in 100 CLT projects in the UK. The project data were acquired from an industry publication and decoded in the form of a multimode project-company network, which was projected into a single-mode company collaborative network. This complete network was filtered into a four-phase network to allow the longitudinal analysis of the CLT collaborations over time. A set of network and node social network analysis metrics was used to characterize the topology patters of the network and the centrality of the companies. Findings The study highlighted the scale-free structure of the CLT collaborative network that depends on the influential hubs of timber manufacturers, engineers and contractors to accelerate the innovation diffusion. However, such CLT supply collaborative network structure is more vulnerable to disruptions due to its dependence on these few prominent hubs. Also, the industry collaborative network’s decreased modularity confirms the maturity of the CLT technology and the formation of cohesive clusters of innovation partners. The macro analysis approach of the study highlighted the critical role of supply chain upstream stakeholders due to their higher centralities in the collaborative network. Stronger collaborations were found between the supply chain upstream stakeholders (timber manufacturers) and downstream stakeholders (architects and main contractors). Originality/value The study contributes to the field of industrialized and CLT construction by characterizing the collaborative networks between CLT supply chain stakeholders that are critical to propose governmental policies and industry initiatives to advance this sustainable construction material.

THE INFLUENCE OF FIRM KNOWLEDGE CHARACTERISTICS ON TECHNOLOGICAL INNOVATION: A MULTILEVEL NETWORK STRUCTURE PERSPECTIVE

This study describes collaboration between firms and different organizations as a process of knowledge sharing, integrating inter-organizational collaboration networks and knowledge networks into a unified framework. It analyzes the bilayer structure of collaborative innovation networks, namely, the firm-knowledge bipartite network. The study investigates the impact of firm knowledge characteristics (diversity and uniqueness) on breakthrough and incremental innovation within collaborative innovation networks. The research employs patent data from Chinese A-share high-tech manufacturing companies listed from 2000 to 2018 and verifies the related hypotheses using a negative binomial regression model. The results suggest that, within collaborative innovation networks, firm knowledge diversity has a negative impact on breakthrough innovation but a positive impact on incremental innovation. On the other hand, firm knowledge uniqueness plays a positive role in breakthrough innovation but a negative role in incremental innovation. This study extends the understanding of firm technological innovation by considering both breakthrough and incremental innovation as distinct behaviors, providing a new perspective on the mechanisms underlying different innovation behaviors. By adopting a multilevel network structure approach to collaborative innovation, it contributes to a deeper understanding of internal and external factors influencing firm innovation behavior and expands the application scope of social network analysis.

Platform collaboration patterns, network structure, and innovation performance: An NK simulation approach

Prior research lacks understanding about how collaborative arrangements and network structures influence innovation outcomes in platform-based ecosystems. This paper addresses this gap by using an NK simulation model to investigate the impacts of different collaboration patterns and network structures on innovation performance. The NK simulation approach overcomes the shortcomings of empirical methods and enables examining the dynamic impacts. The results reveal that the “special platform + generic complementor” pattern leads to the highest innovation output. The impact of component correlations on the innovation performance follows an inverted U-shape. The small world network structure promotes innovation versus regular or random networks. The results provide novel theoretical insights into strategically configuring platform partnerships and network connections to optimize innovation. The findings offer practical guidance for firms to choose beneficial collaboration pattern and design proper network structure.

Strong collaborative governance networks support effective Forest Stewardship Council-certified community-based forest management: Evidence from Southeast Tanzania

Research on community-based forest management indicates its conservation outcomes depend on local rule enforcement, extraction pressures, and community support. However, many community-based forest management projects, particularly in the Global South, also involve collaborative networks of non-state actors such as NGOs and private corporations. Many of these networks promote sustainability certification under programs like the Forest Stewardship Council. We report on analyses of longitudinal forest cover data constructed using satellite observations alongside inter-organizational collaborative governance network data constructed from archival sources, document analysis, and oral histories to assess how collaborative governance networks shape community-based forestry’s conservation effectiveness in eight villages in Kilwa District, Tanzania. Our findings indicate certified community-based forestry’s impacts on deforestation can depend on the composition and structure of collaborative governance networks. Using matched Cox proportional hazards models with geographic fixed effects, we find evidence that certified community-based forest management can stem forest loss as effectively as state-led forest management (in the form of National Forest Reserves). However, the characteristics of collaborative governance networks connecting organizations engaged in forest management in our study villages shape both which areas are selected into certified community-based forest management and villages’ overall deforestation rates. Specifically, we find that the more each village government’s organizational partners are connected to one another through bonding ties, and the more civil society organizations collaborate with each village government through bridging ties, the lower the village’s deforestation risk. More private sector organizations connected to village governments through bridging ties, however, are associated with higher deforestation risks. Our evidence highlights the importance of investments in inter-organizational networks for promoting sustainably certified community-based forest conservation.

FCUnet: Refined remote sensing image segmentation method based on a fuzzy deep learning conditional random field network

AbstractImage segmentation is pivotal for the understanding of high‐resolution remote sensing images (HRRS). However, because of the inherent uncertainties in remote sensing images and the highly complex resolution of HRRS, ambiguity often occurs among some geographic entities in the segmentation process, and the fine segmentation of HRRS is not considered sufficiently for most existing segmentation methods. Therefore, in this paper, the authors propose a new collaborative neural network structure called fuzzy deep learning conditional random field network (FCUnet) to solve the refined segmentation of HRRS. First, the authors design a fuzzy U‐Net classification network to obtain effective feature information, which introduces the fuzzy logic unit into the network to process the ambiguity and uncertainty of HRRS. Then, the authors introduce the conditional random field (CRF) at the end of FCUnet to optimize the image segmentation results. Finally, the authors validated the effectiveness and superiority of their approach on three data sets. The experiment results revealed that FCUnet had better refined segmentation performance and generalization ability than state‐of‐the‐art methods.

Differences in collaboration structures and impact among prominent researchers in Europe and North America

Scientists collaborate through intricate networks, which impact the quality and scope of their research. At the same time, funding and institutional arrangements, as well as scientific and political cultures, affect the structure of collaboration networks. Since such arrangements and cultures differ across regions in the world in systematic ways, we surmise that collaboration networks and impact should also differ systematically across regions. To test this, we compare the structure of collaboration networks among prominent researchers in North America and Europe. We find that prominent researchers in Europe establish denser collaboration networks, whereas those in North America establish more decentralized networks. We also find that the impact of the publications of prominent researchers in North America is significantly higher than for those in Europe, both when they collaborate with other prominent researchers and when they do not. Although Europeans collaborate with other prominent researchers more often, which increases their impact, we also find that repeated collaboration among prominent researchers decreases the synergistic effect of collaborating.

Exploring the Project-Based Collaborative Networks between Owners and Contractors in the Construction Industry: Empirical Study in China

In the project-based construction industry, organizations build collaborative relationships through specific projects. The owners and contractors who are the key project stakeholders have gradually formed a complex project-based industry-level collaborative network in many different projects, closely related to knowledge exchange and industry development. Based on the data set of the National Quality Engineering Award (NQEA) projects in China from 2013 to 2021, we empirically analyze the characteristics and evolution of project-based collaborative networks between owners and contractors in the construction industry by using social network analysis (SNA) and network motif analysis (NMA) method. The results show that (1) the owner–contractor collaborative network exhibits small-world network characteristics. The island effect caused by small groups in the network makes the overall connectivity of the network low. During the study period, the collaborative network became more compact. (2) State-owned construction companies, such as China Construction Third Engineering Bureau Corporation Limited, China Construction Eighth Engineering Bureau Corporation Limited, and China Construction Second Engineering Bureau Corporation Limited, with high degree centrality and betweenness centrality, are the core companies in the collaborative network. In China, state-owned construction enterprises are favored by owners and have established collaborative relationships with many owners and contractors. (3) There are two local collaborative patterns in the collaborative network: motif and anti-motif. Motifs include some triangle-based tight collaborative patterns, while anti-motifs involve some loose binary collaborative patterns. The results help understand the structure and evolution of the industry-level collaborative relationship network between owners and contractors and can provide references for owners and contractors to develop relationship cultivation strategies more effectively.

Decentralized Edge Intelligence-Driven Network Resource Orchestration Mechanism

With the development of artificial intelligence of things (AIoT), multi-access edge computing (MEC) becomes a key enabler to migrate cloud services to edge clients. In comparison to traditional cloud computing techniques, MEC is characterized with low transmission latency, good flexibility, adaptability and robustness. Nevertheless, traditional resource allocation methods are difficult to meet the requirements of achieving a ubiquitous, pervasive, and intelligent computation offloading strategy in highdynamic network environments. In this paper, we construct an edge intelligence-enabled cloud-edge-client collaborative network structure, and conceive a model-aided multi-agent deep deterministic policy gradient (MA2DDPG) computation offloading framework relying on both centralized training and distributed execution. Simulation results corroborate that our proposed decentralized resource orchestration platform significantly reduces the energy consumption and the transmission latency against state-of-the-art methods. Finally, we highlight open challenges and potential solutions.

Targeted Community Merging provides an efficient comparison between collaboration clusters and departmental partitions

Abstract Community detection theory is vital for the structural analysis of many types of complex networks, especially for human-like collaboration networks. In this work, we present a new community detection algorithm, the Targeted Community Merging algorithm, based on the well-known Girvan–Newman algorithm, which allows obtaining community partitions with high values of modularity and a small number of communities. We then perform an analysis and comparison between the departmental and community structure of scientific collaboration networks within the University of Zaragoza. Thus, we draw valuable conclusions from the inter- and intra-departmental collaboration structure that could be useful to take decisions on an eventual departmental restructuring.

The structure and dynamics of instrument collaboration networks

Complex scientific questions often require collaboration between scientists to access scientific instruments (deS. Price, Res Policy 13:3–20, 1984; Shrum et al. 2007, Structures of scientific collaboration, The MIT Press, 2007), knowledge and social capital from scientists outside of their immediate networks (Burt, Am J Sociol 110:349–399, 2004; Collins, Tacit and explicit knowledge, University of Chicago Press, https://press.uchicago.edu/ucp/books/book/chicago/T/bo8461024.html, 2012; Granovetter, Am J Sociol 78:1360–1380, 1973; Polanyi, Personal knowledge: towards a post-critical philosophy (Repr. (with corr.)). Routledge & Kegan Paul, 1962). Synchrotron radiation facilities are a unique case to explore this type of collaboration, as external scientists going to the facility to do their ordinary research work are common. These external users use complex scientific instrumentation that could require the additional accumulated knowledge of internal scientists and staff more familiar with the technical and social aspects of the instruments. These collaborations sometimes result in a co-authored publication with internal staff, employed in these facilities. However, this is not always the case. Data from the European Synchrotron Radiation Facility (ESRF) shows that most often, external scientists do not include internal staff in the co-author fields of the subsequent publication from scientific work done in one (or more) instruments in these facilities. Instrument collaboration networks are constructed and analysed over the period 2000–2018 for the different scientific instruments within the facility. A strong relationship between the level of external collaboration and the structure of the networks is found and explored. The results provide further insight into factors that shape collaboration and knowledge transfer, also relevant to policy makers and facility managers seeking to promote these activities.

Regular collective dynamics of research collaboration

Research collaboration, a ubiquitous phenomenon marking the increasing stratification of science, is hailed for its intellectual and societal benefits. Being an essential element of the economics of science, it has become a matter of scientific inquiry attracting efforts from fields as different as network analysis and bibliometrics. The studies have provided insights into the local structure of collaboration networks, relations between the team composition and research impact. However, the laws governing research collaboration at the (macro)scale of countries and institutions are yet to be established. In this work, a simple yet unexpectedly accurate logarithmic relation between measures of the research output and self-reliance is demonstrated. Restriction of co-authorship analysis to high-ranking journals suppresses geographical and cultural differences to expose universalities in the international research collaboration. The emerging law holds across research areas; it benchmarks collaboration patterns of research institutions grouped by type or country. The self-organized regularity of the collaboration dynamics manifests an important role of national research systems in globalized science, may have profound implications for research policy development and implementation.

Social network analysis to understand the dynamics of global supply chains

PurposeThe aim of this study is to increase the understanding of collaborative relationships and assess according to the project size, the influence of the contributory factors in shaping collaboration network structure in projects developed in global supply chains (GSC).Design/methodology/approachThe paper used a case study methodology applied to eight global projects developed by an Austrian company leader in global market intra-logistics solutions and warehouse automation. The cases were studied by two approaches in network analysis. First, visual and descriptive analysis to describe structural aspects of the network. Second, stochastic network analysis to evaluate the influence of contributory factors in the structure of the collaboration network.FindingsThe results evidence that independently of the project size and project manager influence, project team roles (PTR) who have a reciprocal communication among other PTR tend to have a higher collaboration intensity (CI). Additionally, the results highlight the influence of the project manager in shaping the collaboration network in standard projects (STP) and small projects (SMP). According to the project size, the results show that the PTR that form complete triangles or cluster or who communicate frequently among each other tend to have a high CI, being more evident these tendencies in large-scale projects than STP and SMP.Originality/valueThis research provides a framework to identify the key actors and contributory factors in shaping collaborative relationships in GSC. The findings could be used to support the decision-making process and formulation strategies for effective collaborative relationship management in GSC.

From Ideas to Impact: The Process and Promise of Democratized Innovation in Healthcare

Decentralized, distributed systems of innovation and production are becoming ever more economically and strategically important. This has prompted a surge of interest among strategy and organizations scholars, with rich bodies of work emerging around open innovation, platforms, ecosystems, the sharing economy, user innovation, and more. One recent, yet understudied manifestation of this broader trend is the maker movement, which refers to collaborative communities of amateur and professional fabricators and tinkerers that have emerged over the past two decades. The goal of this symposium is to explore the technologies and practices associated with this community from a processual perspective, specifically in the context of healthcare innovation. We do so by showcasing cutting-edge unpublished work from top scholars publishing in the areas of technology, organizational learning, and entrepreneurship. These papers adopt different theoretical perspectives and levels of analysis to explore a set of closely-related, under-researched, and important questions: How can teams use “democratizing” technologies to identify new opportunities, and how can emergent organizations enact those ideas to create value? How do distributed, collaborative network structures emerge, and how does knowledge flow through them over time? Collectively, these papers draw insights from user innovation and the maker movement context to advance research and theory on entrepreneurship, legitimacy, ecosystems, and organizational emergence. Following the four papers, an expert discussant will synthesize emergent insights, and we will conclude with an exercise in “democratized innovation” of our own, using a group exercise to crowdsource a collective future research agenda. How maker tools can accelerate ideation Presenter: Sarah Lebovitz; U. of Virginia Pandemic makers: Emergent networks of citizen crisis response Presenter: Russell E. Browder; U. of Oklahoma Price College of Business Presenter: Angela Forgues; U. of Wisconsin Presenter: Stella Seyb; U. of Oklahoma Presenter: Howard Aldrich; U. of North Carolina Emergence of a grassroots ecosystem in response to the PPE supply crisis Presenter: Douglas Hannah; Boston U. Presenter: Ya-Ching Huang; Boston U. Stoking the flames of innovation: Differential knowledge contributions in medical imaging Presenter: Uisung David Park; Syracuse U. Presenter: Shinjinee Chattopadhyay; U. of Illinois Presenter: Sonali Shah; U. of Illinois at Urbana-Champaign

Intentional sustainable communities and sustainable development goals: from micro-scale implementation to scalability of innovative practices

Intentional sustainable communities (ISCs) are commonly described as micro-based community initiatives that seek to develop sustainable lifestyles with low environmental impact. More recently, they have been analyzed as laboratories for the emergence of innovation, namely social innovation, and as actors that can contribute to sustainable transitions. This reinforces their role as pressure agents and as microscale communities of practice. Theoretically, it is argued that this scalability of innovation dynamics requires a multi-level and multi-actor perspective, mechanisms of reciprocity and that actors are not isolated and atomized. These mechanisms, such as networks, are crucial for the dissemination of sustainability practices and for increasing their potential impact. This article argues that these communities can also be important micro agents in achieving Sustainable Development Goals (SDGs) and to mainstream the importance of scaling their innovation practices. To this end, the study uses quantitative data from a survey applied to the ISCs in Europe. The data corroborates the communities’ contribution to the SDGs, namely to SDG12, SDG13 and SDG16 and presents the collaboration network structure established within these communities.

Formation and Evolution of Ideal Interfirm Collaborative Innovation Networks Based on Decision-Making Rules for Partner Selection

On the basis of an external and static perspective on the topological structure of collaborative innovation networks, it is extremely difficult to answer the two most important concerns, namely, which structure is ideal and how to develop it in practice. By contrast, this study transfers to internal and dynamic perspectives, and then proposes that the essence of developing the ideal network lies in choosing the best partners. Therefore, we firstly propose the basic decision-making rules for selecting partners. In order of priority: knowledge distance, knowledge complementarity and barter exchange. Secondly, a model is constructed to describe this process of selecting partners and exchanging knowledge. Thirdly, the simulation results show that a small-world network is ideal in the initial stage of collaborative innovation. However, a random network is ideal in the mature periods. This result shows that the ideal network structure is not fixed, but affected by the life cycle of collaborative innovation alliance. Furthermore, this supports the notion that a small world is spontaneously generated in the real world, and also confirms that the formation of a small-world network will be driven intrinsically by a firm’s demand for external knowledge, and not necessarily by the external driving force of social capital. Finally, these findings solve the above two most important questions.

Are collaborative challenges barriers to working together? – A multi-level multi-case network analysis

Public and nonprofit organizations often face various challenges when collaborating with others. The existing literature emphasizes the danger of collaborative challenges, and an implicit assumption is that collaborative challenges will affect inter-organizational relationships and network structures. Using data from three different purpose-oriented networks, this study explores the relationship between perceptions of collaborative challenges and network structure across the node, dyadic, and whole network level of analyses. The multi-case, multi-level analysis indicates that there is no pattern between perceived challenges and network structures. Organizations that perceive more challenges are not necessarily more resistant to work with other organizations. This counterintuitive null finding offers important implications for theory and practice. Network managers should be encouraged to embrace the complex or even paradoxical nature of collaboration. Emphasizing collaborative incentives may have greater impact than focusing on clearing challenges that are inherent in collaboration.