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1. |
Record Nr. |
UNINA9910422644603321 |
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Autore |
Berglund Oscar |
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Titolo |
Extinction Rebellion and Climate Change Activism : Breaking the Law to Change the World / / by Oscar Berglund, Daniel Schmidt |
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Pubbl/distr/stampa |
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Cham : , : Springer International Publishing : , : Imprint : Palgrave Macmillan, , 2020 |
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ISBN |
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Edizione |
[1st ed. 2020.] |
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Descrizione fisica |
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1 online resource (VIII, 109 p. 1 illus.) |
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Disciplina |
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Soggetti |
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Political sociology |
Environmental policy |
Political Sociology |
Environmental Policy |
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Lingua di pubblicazione |
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Formato |
Materiale a stampa |
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Livello bibliografico |
Monografia |
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Nota di contenuto |
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1. Introduction -- 2. XR and Anarchism -- 3. Civility and Disobedience -- 4. Between Democracy and Efficiency -- 5. Reimagining Democracy -- 6. A Theory of Change: The Civil Resistance Model -- 7. Conclusion: XR, The Climate Change Movement and Capitalism. . |
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Sommario/riassunto |
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This book summarises and critiques Extinction Rebellion (XR) as a social movement organisation, engaging with key issues surrounding its analysis, strategy and tactics. The authors suggest that XR have an underdeveloped and apolitical view of the kind of change necessary to address climate change, and suggest that while this enables the building of broad movements, it is also an obstacle to achieving the systemic change that they are aiming for. The book analyses different forms of protest and the role of civil disobedience in their respective success or failure; democratic demands and practices; and activist engagement with the political economy of climate change. It engages with a range of theoretical perspectives that address law-breaking in protest and participatory forms of democracy including liberal political theory; anarchism and forms of historical materialism, and will be of |
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interest to students and scholars across politics, international relations, sociology, policystudies and geography, as well as those interested in climate change politics and activism. Oscar Berglund is Lecturer in International Public and Social Policy at the University of Bristol, UK. Daniel Schmidt is a MSc graduate in Public Policy from the University of Bristol, UK. |
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2. |
Record Nr. |
UNINA9910743356503321 |
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Autore |
Shi Chuan |
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Titolo |
Heterogeneous Graph Representation Learning and Applications / / by Chuan Shi, Xiao Wang, Philip S. Yu |
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Pubbl/distr/stampa |
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Singapore : , : Springer Nature Singapore : , : Imprint : Springer, , 2022 |
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ISBN |
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981-16-6166-9 |
981-16-6165-0 |
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Edizione |
[1st ed. 2022.] |
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Descrizione fisica |
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1 online resource (329 pages) |
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Collana |
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Artificial Intelligence: Foundations, Theory, and Algorithms, , 2365-306X |
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Disciplina |
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Soggetti |
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Data mining |
Machine learning |
Artificial intelligence - Data processing |
Data Mining and Knowledge Discovery |
Machine Learning |
Data Science |
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Lingua di pubblicazione |
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Formato |
Materiale a stampa |
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Livello bibliografico |
Monografia |
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Nota di bibliografia |
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Includes bibliographical references. |
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Nota di contenuto |
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Introduction -- The State-of-the-art of Heterogeneous Graph Representation -- Part One: Techniques -- Structure-preserved Heterogeneous Graph Representation -- Attribute-assisted Heterogeneous Graph Representation -- Dynamic Heterogeneous Graph Representation -- Supplementary of Heterogeneous Graph Representation -- Part Two: Applications -- Heterogeneous Graph Representation for Recommendation -- Heterogeneous Graph Representation for Text Mining -- Heterogeneous Graph |
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Representation for Industry Application -- Future Research Directions -- Conclusion. . |
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Sommario/riassunto |
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Representation learning in heterogeneous graphs (HG) is intended to provide a meaningful vector representation for each node so as to facilitate downstream applications such as link prediction, personalized recommendation, node classification, etc. This task, however, is challenging not only because of the need to incorporate heterogeneous structural (graph) information consisting of multiple types of node and edge, but also the need to consider heterogeneous attributes or types of content (e.g. text or image) associated with each node. Although considerable advances have been made in homogeneous (and heterogeneous) graph embedding, attributed graph embedding and graph neural networks, few are capable of simultaneously and effectively taking into account heterogeneous structural (graph) information as well as the heterogeneous content information of each node. In this book, we provide a comprehensive survey of current developments in HG representation learning. Moreimportantly, we present the state-of-the-art in this field, including theoretical models and real applications that have been showcased at the top conferences and journals, such as TKDE, KDD, WWW, IJCAI and AAAI. The book has two major objectives: (1) to provide researchers with an understanding of the fundamental issues and a good point of departure for working in this rapidly expanding field, and (2) to present the latest research on applying heterogeneous graphs to model real systems and learning structural features of interaction systems. To the best of our knowledge, it is the first book to summarize the latest developments and present cutting-edge research on heterogeneous graph representation learning. To gain the most from it, readers should have a basic grasp of computer science, data mining and machine learning. |
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