Shifu2 : a network representation learning based model for advisor-advisee relationship mining
- Liu, Jiaying, Xia, Feng, Wang, Lei, Xu, Bo, Kong, Xiangjie
- Authors: Liu, Jiaying , Xia, Feng , Wang, Lei , Xu, Bo , Kong, Xiangjie
- Date: 2021
- Type: Text , Journal article
- Relation: IEEE Transactions on Knowledge and Data Engineering Vol. 33, no. 4 (2021), p. 1763-1777
- Full Text:
- Reviewed:
- Description: The advisor-advisee relationship represents direct knowledge heritage, and such relationship may not be readily available from academic libraries and search engines. This work aims to discover advisor-advisee relationships hidden behind scientific collaboration networks. For this purpose, we propose a novel model based on Network Representation Learning (NRL), namely Shifu2, which takes the collaboration network as input and the identified advisor-advisee relationship as output. In contrast to existing NRL models, Shifu2 considers not only the network structure but also the semantic information of nodes and edges. Shifu2 encodes nodes and edges into low-dimensional vectors respectively, both of which are then utilized to identify advisor-advisee relationships. Experimental results illustrate improved stability and effectiveness of the proposed model over state-of-the-art methods. In addition, we generate a large-scale academic genealogy dataset by taking advantage of Shifu2. © 1989-2012 IEEE.
- Authors: Liu, Jiaying , Xia, Feng , Wang, Lei , Xu, Bo , Kong, Xiangjie
- Date: 2021
- Type: Text , Journal article
- Relation: IEEE Transactions on Knowledge and Data Engineering Vol. 33, no. 4 (2021), p. 1763-1777
- Full Text:
- Reviewed:
- Description: The advisor-advisee relationship represents direct knowledge heritage, and such relationship may not be readily available from academic libraries and search engines. This work aims to discover advisor-advisee relationships hidden behind scientific collaboration networks. For this purpose, we propose a novel model based on Network Representation Learning (NRL), namely Shifu2, which takes the collaboration network as input and the identified advisor-advisee relationship as output. In contrast to existing NRL models, Shifu2 considers not only the network structure but also the semantic information of nodes and edges. Shifu2 encodes nodes and edges into low-dimensional vectors respectively, both of which are then utilized to identify advisor-advisee relationships. Experimental results illustrate improved stability and effectiveness of the proposed model over state-of-the-art methods. In addition, we generate a large-scale academic genealogy dataset by taking advantage of Shifu2. © 1989-2012 IEEE.
Citizen science: Knowledge, networks and the boundaries of participation
- Authors: Bonney, Patrick
- Date: 2020
- Type: Text , Thesis , PhD
- Full Text:
- Description: The water-related challenges facing humanity are complex and urgent. Although solutions are not always clear, involving the public in localised knowledge production and policy development is widely recognised as a critical part of this larger effort. Such public engagement is increasingly achieved through “citizen science”—a practice that involves non-professionals in scientific research and monitoring. Academic literature has recognised that, while citizen science is both important and necessary to strengthen environmental policy, its acceptance and successful implementation is a difficult governance challenge. Researchers agree that overcoming this challenge depends on the ability of volunteers, coordinators, scientists and decision-makers to work together to convert the potential of citizen science into practice. However, little is known about the collaborative relationships or the broader social contexts that shape and define the practice. To address these shortfalls, this thesis advances a conceptual framework for the relational analysis of citizen science that illustrates social networks and the boundaries between expert and community-based knowledge as critical sites of investigation. Through its multi-phase and mixed-methods research design, the findings of this thesis shed light on the contributions of citizen science to key waterway governance objectives, including the social, political and cultural factors that influence its acceptance and uptake in governance contexts. By unpacking the relational dimensions of citizen science, this thesis provides both theoretical and practice-based insights into how actors within and outside citizen science programs work together to achieve collective aims to engender stronger connections between science, society and policy. This thesis will benefit practitioners, policymakers and participatory advocates interested in achieving practical social change in efforts to understand and manage natural resources.
- Description: Doctor of Philosophy
- Authors: Bonney, Patrick
- Date: 2020
- Type: Text , Thesis , PhD
- Full Text:
- Description: The water-related challenges facing humanity are complex and urgent. Although solutions are not always clear, involving the public in localised knowledge production and policy development is widely recognised as a critical part of this larger effort. Such public engagement is increasingly achieved through “citizen science”—a practice that involves non-professionals in scientific research and monitoring. Academic literature has recognised that, while citizen science is both important and necessary to strengthen environmental policy, its acceptance and successful implementation is a difficult governance challenge. Researchers agree that overcoming this challenge depends on the ability of volunteers, coordinators, scientists and decision-makers to work together to convert the potential of citizen science into practice. However, little is known about the collaborative relationships or the broader social contexts that shape and define the practice. To address these shortfalls, this thesis advances a conceptual framework for the relational analysis of citizen science that illustrates social networks and the boundaries between expert and community-based knowledge as critical sites of investigation. Through its multi-phase and mixed-methods research design, the findings of this thesis shed light on the contributions of citizen science to key waterway governance objectives, including the social, political and cultural factors that influence its acceptance and uptake in governance contexts. By unpacking the relational dimensions of citizen science, this thesis provides both theoretical and practice-based insights into how actors within and outside citizen science programs work together to achieve collective aims to engender stronger connections between science, society and policy. This thesis will benefit practitioners, policymakers and participatory advocates interested in achieving practical social change in efforts to understand and manage natural resources.
- Description: Doctor of Philosophy
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