3,365 research outputs found

    Network representation learning enhanced by partial community information that is found using game theory

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    Presently, data that are collected from real systems and organized as information networks are universal. Mining hidden information from these data is generally helpful to understand and benefit the corresponding systems. The challenges of analyzing such data include high computational complexity and low parallelizability because of the nature of complicated interconnected structure of their nodes. Network representation learning, also called network embedding, provides a practical and promising way to solve these issues. One of the foremost requirements of network embedding is preserving network topology properties in learned low-dimension representations. Community structure is a prominent characteristic of complex networks and thus should be well maintained. However, the difficulty lies in the fact that the properties of community structure are multivariate and complicated; therefore, it is insufficient to model community structure using a predefined model, the way that is popular in most state-of-the-art network embedding algorithms explicitly considering community structure preservation. In this paper, we introduce a multi-process parallel framework for network embedding that is enhanced by found partial community information and can preserve community properties well. We also implement the framework and propose two node embedding methods that use game theory for detecting partial community information. A series of experiments are conducted to evaluate the performance of our methods and six state-of-the-art algorithms. The results demonstrate that our methods can effectively preserve community properties of networks in their low-dimension representations. Specifically, compared to the involved baselines, our algorithms behave the best and are the runners-up on networks with high overlapping diversity and density

    On the human impacts and governance of large-scale tree plantations

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    Because of the pace and magnitude of land cover change, terrestrial ecosystems across the globe are under unprecedented pressure. Industrial production of wood in large-scale tree plantations is one of the drivers of this change. The development of funds of natural capital on private lands for marketable commodities, however, often comes at the expense of other non-marketable benefits that people derive from ecosystems. The disturbances to existing ecosystems and social systems caused by the establishment of plantations can be drastic. Identifying factors that foster and impede actors and institutions to solve problems and address injustices thus becomes crucial for advancing sustainability through changes in policies and practices. This dissertation synthesises findings from four articles. It takes on the task of filling two gaps in the previous scholarly literature: the first concerning the human impacts of large-scale tree plantations (articles I and II); the second concerning the different institutions that shape their governance (articles III and IV). It also brings these contributions together under a framework for empirical analysis, which combines and structures key concepts of environmental social sciences ranging from systems ecology to sociology. Both qualitative and quantitative research methods have been used in the four articles. Article I presents the findings from a systematic review of the impacts of large-scale tree plantations for local communities. The review shows that impacts are frequently grounded in the process of land acquisition for plantations and the subsequent loss of livelihoods. Plantations have often caused more losses of livelihoods than created jobs. Article I also identifies gaps in the evidence base. Article II applies the concept of resilience and qualitative content analysis to analyse the Uruguayan beekeepers’ experiences of and responses to land cover change to plantations. The results show that the community faces this change as multiple interlinked challenges (e.g., lower honey yields and higher costs), to which they generally have a limited capacity to adapt. Both articles III and IV use data from the domain of South African tree plantation policy. Based on an analysis of policy beliefs, the former identifies two competing coalitions: a dominant business-as-usual coalition, of which ideas a minority justice and change coalition challenges. Article III also clarifies the role that beliefs concerning specific policy instruments play in coalition formation. Article IV focuses on policy learning – the acquisition and dissemination of information between actors with diverse knowledge. It tests hypotheses concerning actors’ information exchange behaviour and finds that actors tend to exchange information and build trust with those who think alike. However, its findings support the idea that co-participation in policy forums enables policy learning. Large-scale tree plantations have often caused negative impacts for local communities. The unfolding of impacts, however, also depends on the context (e.g., land use rights). The impacts are in many ways rooted in the governance of plantations, the dynamics of which can be better understood through coalition formation and policy learning

    On the human impacts and governance of large-scale tree plantations

    Get PDF
    Because of the pace and magnitude of land cover change, terrestrial ecosystems across the globe are under unprecedented pressure. Industrial production of wood in large-scale tree plantations is one of the drivers of this change. The development of funds of natural capital on private lands for marketable commodities, however, often comes at the expense of other non-marketable benefits that people derive from ecosystems. The disturbances to existing ecosystems and social systems caused by the establishment of plantations can be drastic. Identifying factors that foster and impede actors and institutions to solve problems and address injustices thus becomes crucial for advancing sustainability through changes in policies and practices. This dissertation synthesises findings from four articles. It takes on the task of filling two gaps in the previous scholarly literature: the first concerning the human impacts of large-scale tree plantations (articles I and II); the second concerning the different institutions that shape their governance (articles III and IV). It also brings these contributions together under a framework for empirical analysis, which combines and structures key concepts of environmental social sciences ranging from systems ecology to sociology. Both qualitative and quantitative research methods have been used in the four articles. Article I presents the findings from a systematic review of the impacts of large-scale tree plantations for local communities. The review shows that impacts are frequently grounded in the process of land acquisition for plantations and the subsequent loss of livelihoods. Plantations have often caused more losses of livelihoods than created jobs. Article I also identifies gaps in the evidence base. Article II applies the concept of resilience and qualitative content analysis to analyse the Uruguayan beekeepers’ experiences of and responses to land cover change to plantations. The results show that the community faces this change as multiple interlinked challenges (e.g., lower honey yields and higher costs), to which they generally have a limited capacity to adapt. Both articles III and IV use data from the domain of South African tree plantation policy. Based on an analysis of policy beliefs, the former identifies two competing coalitions: a dominant business-as-usual coalition, of which ideas a minority justice and change coalition challenges. Article III also clarifies the role that beliefs concerning specific policy instruments play in coalition formation. Article IV focuses on policy learning – the acquisition and dissemination of information between actors with diverse knowledge. It tests hypotheses concerning actors’ information exchange behaviour and finds that actors tend to exchange information and build trust with those who think alike. However, its findings support the idea that co-participation in policy forums enables policy learning. Large-scale tree plantations have often caused negative impacts for local communities. The unfolding of impacts, however, also depends on the context (e.g., land use rights). The impacts are in many ways rooted in the governance of plantations, the dynamics of which can be better understood through coalition formation and policy learning

    Modeling Complex Networks For (Electronic) Commerce

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    NYU, Stern School of Business, IOMS Department, Center for Digital Economy Researc

    A network theoretic perspective of decision processes in complex construction projects

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    This paper proposes an approach to modelling and visualising decision processes in large complex construction projects by incorporating a network perspective. Computer modelling and visualisation of decision processes as social and task-entity networks makes possible the identification of key participants, critical tasks, latent networks, vulnerabilities and dynamics that impact upon complex decision situations. New advances in network theory can help reveal the ways in which social, organisational, political and technological relationships shape decision outcomes. By conceiving decision processes as a complex system and modelling this system using network-theoretic principles, it is possible to include a tremendous amount of information that has remained untapped by conventional qualitative, game-theoretic, and statistical approaches. This research contributes to the understanding of the strategic implications of decision processes as complex systems of interacting actors and problem tasks, and provides the technological means for supporting them. The approach has been verified through the development of an experimental network-theoretic system

    Coordinating the Competition, Pre-electoral Coalitions in the Indian General Elections

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    The number and variety of pre-electoral coalitions in the Indian general elections make India a prime case to examine why parties chose to join forces with their rivals during elections. Yet, existing theories, which emphasise narrow definitions of party size and shared ideology, are unable to explain the tangled alliances that emerge between Indian political parties. In order to examine why parties pursue certain pre-electoral coalitions, I employ a mixed-methods strategy that combines statistical network analysis (exponential random graph models) with case study analysis, using a new dataset of pre-electoral coalitions 1999-2014. The network analysis suggests that pre-electoral coalitions in India are driven by the parties’ wish to increase their odds of winning in particular constituencies and, to a smaller degree, their wish to combine their parliamentary strength afterwards. The analysis also suggests that the network structure of the party system has a significant impact on pre- electoral coalition formation in that parties are attracted to ‘high-connector parties’ that allow them to form indirect alliances with a number of parties, and that parties build denser, regional coalitions that allow smaller parties to buy leverage against bigger allies. Finally, even though pre-electoral coalitions in India appear highly changeable, parties are more likely to renew an existing pre-electoral coalition than to build a new one. I explore the implications of the network analysis in three case studies, namely a pre- electoral coalition that took place as the model predicted (a true positive case), one that did not take pace despite being predicted (a false positive case), and one that took place despite not being predicted (a false negative case). The case studies corroborate the statistical findings but also demonstrate that network structures can both encourage and hinder pre-electoral coalitions
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