The Structure of Communities in Successful Bollywood Movies: Analyzing the Collaboration Network of Actors & Directors

2020 ◽  
Author(s):  
Samrat Gupta ◽  
Amit Anand Tiwari
Keyword(s):  
Collaboration Network ◽  
Structure Of Communities

Health Informatics Education in the Third World

1989 ◽  
Vol 28 (04) ◽  
pp. 270-272 ◽  
Author(s):  
O. Rienhoff
Keyword(s):  
Developing Countries ◽  
Health Informatics ◽  
Third World ◽  
State Of The Art ◽  
The State ◽  
Collaboration Network ◽  
Educational Tools ◽  
The Third ◽  
The Third World

Abstract:The state of the art is summarized showing many efforts but only few results which can serve as demonstration examples for developing countries. Education in health informatics in developing countries is still mainly dealing with the type of health informatics known from the industrialized world. Educational tools or curricula geared to the matter of development are rarely to be found. Some WHO activities suggest that it is time for a collaboration network to derive tools and curricula within the next decade.

scholarly journals Multi-hop assortativities for network classification

2018 ◽  
Vol 7 (4) ◽  
pp. 603-622 ◽  
Author(s):  
Leonardo Gutiérrez-Gómez ◽  
Jean-Charles Delvenne
Keyword(s):  
Machine Learning ◽  
Scientific Collaboration ◽  
State Of The Art ◽  
Medical Engineering ◽  
Research Field ◽  
Classification Task ◽  
Collaboration Network ◽  
Structural Patterns ◽  
Art Methods

Abstract Several social, medical, engineering and biological challenges rely on discovering the functionality of networks from their structure and node metadata, when it is available. For example, in chemoinformatics one might want to detect whether a molecule is toxic based on structure and atomic types, or discover the research field of a scientific collaboration network. Existing techniques rely on counting or measuring structural patterns that are known to show large variations from network to network, such as the number of triangles, or the assortativity of node metadata. We introduce the concept of multi-hop assortativity, that captures the similarity of the nodes situated at the extremities of a randomly selected path of a given length. We show that multi-hop assortativity unifies various existing concepts and offers a versatile family of ‘fingerprints’ to characterize networks. These fingerprints allow in turn to recover the functionalities of a network, with the help of the machine learning toolbox. Our method is evaluated empirically on established social and chemoinformatic network benchmarks. Results reveal that our assortativity based features are competitive providing highly accurate results often outperforming state of the art methods for the network classification task.

Attributed Collaboration Network Embedding for Academic Relationship Mining

2021 ◽  
Vol 15 (1) ◽  
pp. 1-20
Author(s):  
Wei Wang ◽  
Jiaying Liu ◽  
Tao Tang ◽  
Suppawong Tuarob ◽  
Feng Xia ◽  
...  
Keyword(s):  
Collaboration Network ◽  
Network Embedding

scholarly journals Is academia becoming more localised? The growth of regional knowledge networks within international research collaboration

2021 ◽  
Vol 6 (1) ◽  
Author(s):  
John Fitzgerald ◽  
Sanna Ojanperä ◽  
Neave O’Clery
Keyword(s):  
Economic Development ◽  
International Collaboration ◽  
International Research ◽  
Research Collaboration ◽  
Learning By Doing ◽  
Knowledge Networks ◽  
Collaboration Network ◽  
International Research Collaboration ◽  
The Past ◽  
Capability Building

AbstractIt is well-established that the process of learning and capability building is core to economic development and structural transformation. Since knowledge is ‘sticky’, a key component of this process is learning-by-doing, which can be achieved via a variety of mechanisms including international research collaboration. Uncovering significant inter-country research ties using Scopus co-authorship data, we show that within-region collaboration has increased over the past five decades relative to international collaboration. Further supporting this insight, we find that while communities present in the global collaboration network before 2000 were often based on historical geopolitical or colonial lines, in more recent years they increasingly align with a simple partition of countries by regions. These findings are unexpected in light of a presumed continual increase in globalisation, and have significant implications for the design of programmes aimed at promoting international research collaboration and knowledge diffusion.

Multi-level social-ecological networks in a payments for ecosystem services programme in central Veracruz, Mexico

2020 ◽  
pp. 1-7
Author(s):  
Alfonso Langle-Flores ◽  
Adriana Aguilar Rodríguez ◽  
Humberto Romero-Uribe ◽  
Julia Ros-Cuéllar ◽  
Juan José Von Thaden
Keyword(s):  
Ecosystem Services ◽  
Spatial Scales ◽  
Payments For Ecosystem Services ◽  
Collaboration Network ◽  
Governmental Organizations ◽  
Social Ecological ◽  
Non Governmental Organizations ◽  
Conservation Activity ◽  
Ecological Importance ◽  
Hydrological Services

Summary Payments for ecosystem services (PES) programmes have been considered an important conservation mechanism to avoid deforestation. These environmental policies act in social and ecological contexts at different spatial scales. We evaluated the social-ecological fit between stakeholders and ecosystem processes in a local PES programme across three levels: social, ecological and social-ecological. We explored collaboration among stakeholders, assessed connectivity between forest units and evaluated conservation activity links between stakeholders and forest units. In addition, to increase programme effectiveness, we classified forest units based on their social and ecological importance. Our main findings suggest that non-governmental organizations occupy brokerage positions between landowners and government in a dense collaboration network. We also found a partial spatial misfit between conservation activity links and the forest units that provide the most hydrological services to Xalapa. We conclude that conservation efforts should be directed towards the middle and high part of the Pixquiac sub-watershed and that the role of non-governmental organizations as mediators should be strengthened to increase the efficiency and effectiveness of the local PES programme.

Scholar2vec: Vector Representation of Scholars for Lifetime Collaborator Prediction

2021 ◽  
Vol 15 (3) ◽  
pp. 1-19
Author(s):  
Wei Wang ◽  
Feng Xia ◽  
Jian Wu ◽  
Zhiguo Gong ◽  
Hanghang Tong ◽  
...  
Keyword(s):  
Scientific Collaboration ◽  
Early Stage ◽  
Collaboration Network ◽  
Vector Representation ◽  
Network Embedding ◽  
Machine Learning Methods ◽  
Academic Networks ◽  
Special Relationships ◽  
Real World Datasets ◽  
Vector Representations

While scientific collaboration is critical for a scholar, some collaborators can be more significant than others, e.g., lifetime collaborators. It has been shown that lifetime collaborators are more influential on a scholar’s academic performance. However, little research has been done on investigating predicting such special relationships in academic networks. To this end, we propose Scholar2vec, a novel neural network embedding for representing scholar profiles. First, our approach creates scholars’ research interest vector from textual information, such as demographics, research, and influence. After bridging research interests with a collaboration network, vector representations of scholars can be gained with graph learning. Meanwhile, since scholars are occupied with various attributes, we propose to incorporate four types of scholar attributes for learning scholar vectors. Finally, the early-stage similarity sequence based on Scholar2vec is used to predict lifetime collaborators with machine learning methods. Extensive experiments on two real-world datasets show that Scholar2vec outperforms state-of-the-art methods in lifetime collaborator prediction. Our work presents a new way to measure the similarity between two scholars by vector representation, which tackles the knowledge between network embedding and academic relationship mining.

Sign in / Sign up

Export Citation Format

Share Document