scholarly journals Interinstitutional Research Team Formation Based on Bibliographic Network Embedding

2021 ◽  
Vol 2021 ◽  
pp. 1-12
Author(s):  
O-Joun Lee ◽  
Seungha Hong ◽  
Jin-Taek Kim
Keyword(s):  
Research Team ◽  
Science And Technology ◽  
Team Formation ◽  
Multilayer Perceptrons ◽  
Network Embedding ◽  
Joint Research ◽  
Network Nodes ◽  
Research Collaborations ◽  
Bibliographic Network ◽  
Vector Representations

This study aims at forming research teams for interinstitutional collaborations. Research institutes have their own purposes and topics of interest. Thus, supporting joint research between multiple institutes, we have to consider not only synergies between scholars but also purposes of the institutes. To solve this problem, we propose a bibliographic network embedding method that can learn characteristics of institutes, not only of each scholar. First, we compose a bibliographic network that consists of scholars, publications, venues, research projects, and institutes. Collaboration styles and research topics of institutes and scholars are extracted by mining subgraphs from the bibliographic network. Then, vector representations of network nodes are learned based on occurrences of subgraphs on the nodes and neighborhoods of the nodes. Based on the vector representations, we train multilayer perceptrons (MLP) to assess collaboration probability between scholars affiliated in different institutes. For training the MLP, we suggest three strategies: (i) considering every collaboration, (ii) focusing on interinstitutional collaborations, and (iii) focusing on collaboration outcomes. To evaluate the proposed methods, we have analyzed research collaborations of POSTECH (Pohang University of Science and Technology) and RIST (Research Institute of Industrial Science and Technology) from 2011 to 2020. Then, we conducted the research team formation for joint research of the two institutes according to two purposes: pure research and commercialization research.

scholarly journals Discrete Network Embedding

2018 ◽  
Author(s):  
Xiaobo Shen ◽  
Shirui Pan ◽  
Weiwei Liu ◽  
Yew-Soon Ong ◽  
Quan-Sen Sun
Keyword(s):  
Large Scale ◽  
State Of The Art ◽  
Ground Truth ◽  
Network Embedding ◽  
Unified Framework ◽  
Network Nodes ◽  
Compact Representations ◽  
Low Dimensional ◽  
Vector Representations ◽  
Embedding Methods

Network embedding aims to seek low-dimensional vector representations for network nodes, by preserving the network structure. The network embedding is typically represented in continuous vector, which imposes formidable challenges in storage and computation costs, particularly in large-scale applications. To address the issue, this paper proposes a novel discrete network embedding (DNE) for more compact representations. In particular, DNE learns short binary codes to represent each node. The Hamming similarity between two binary embeddings is then employed to well approximate the ground-truth similarity. A novel discrete multi-class classifier is also developed to expedite classification. Moreover, we propose to jointly learn the discrete embedding and classifier within a unified framework to improve the compactness and discrimination of network embedding. Extensive experiments on node classification consistently demonstrate that DNE exhibits lower storage and computational complexity than state-of-the-art network embedding methods, while obtains competitive classification results.

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.

scholarly journals Champa Citadels: An Archaeological and Historical Study

2017 ◽  
Vol 5 (2) ◽  
pp. 70-105
Author(s):  
Trường Giang Đỗ ◽  
Tomomi Suzuki ◽  
Văn Quảng Nguyễn ◽  
Mariko Yamagata
Keyword(s):  
Fifteenth Century ◽  
Research Team ◽  
Ninth Century ◽  
Historical Study ◽  
Joint Research ◽  
Field Surveys ◽  
Historical Material ◽  
And Function ◽  
Role And Function ◽  
Archaeological Excavations

Abstract From 2009 to 2012, a joint research team of Japanese and Vietnamese archaeologists led by the late Prof. Nishimura Masanari conducted surveys and excavations at fifteen sites around the Hoa Chau Citadel in Thua Thien Hue Province, built by the Champa people in the ninth century and used by the Viet people until the fifteenth century. This article introduces some findings from recent archaeological excavations undertaken at three Champa citadels: the Hoa Chau Citadel, the Tra Kieu Citadel in Quang Nam Province, and the Cha Ban Citadel in Binh Dinh Province. Combined with historical material and field surveys, the paper describes the scope and structure of the ancient citadels of Champa, and it explores the position, role, and function of these citadels in the context of their own nagaras (small kingdoms) and of mandala Champa as a whole. Through comparative analysis, an attempt is made to identify features characteristic of ancient Champa citadels in general.

scholarly journals Predicting partially observed processes on temporal networks by Dynamics-Aware Node Embeddings (DyANE)

2021 ◽  
Vol 10 (1) ◽  
Author(s):  
Koya Sato ◽  
Mizuki Oka ◽  
Alain Barrat ◽  
Ciro Cattuto
Keyword(s):  
Network Structure ◽  
Machine Learning Algorithms ◽  
Temporal Networks ◽  
Dynamical Processes ◽  
Network Nodes ◽  
Partially Observed ◽  
Low Dimensional ◽  
Vector Representations ◽  
Infectious Disease Dynamics ◽  
Random Times

AbstractLow-dimensional vector representations of network nodes have proven successful to feed graph data to machine learning algorithms and to improve performance across diverse tasks. Most of the embedding techniques, however, have been developed with the goal of achieving dense, low-dimensional encoding of network structure and patterns. Here, we present a node embedding technique aimed at providing low-dimensional feature vectors that are informative of dynamical processes occurring over temporal networks – rather than of the network structure itself – with the goal of enabling prediction tasks related to the evolution and outcome of these processes. We achieve this by using a lossless modified supra-adjacency representation of temporal networks and building on standard embedding techniques for static graphs based on random walks. We show that the resulting embedding vectors are useful for prediction tasks related to paradigmatic dynamical processes, namely epidemic spreading over empirical temporal networks. In particular, we illustrate the performance of our approach for the prediction of nodes’ epidemic states in single instances of a spreading process. We show how framing this task as a supervised multi-label classification task on the embedding vectors allows us to estimate the temporal evolution of the entire system from a partial sampling of nodes at random times, with potential impact for nowcasting infectious disease dynamics.

scholarly journals Special Issue on Infectious Disease Control in SATREPS Projects

2018 ◽  
Vol 13 (4) ◽  
pp. 733-734
Author(s):  
Sumio Shinoda
Keyword(s):  
Infectious Disease ◽  
Developing Countries ◽  
Infectious Diseases ◽  
Disease Control ◽  
Science And Technology ◽  
The Philippines ◽  
Special Issue ◽  
Infectious Disease Control ◽  
Joint Research ◽  
International Joint

The Science and Technology Research Partnership for Sustainable Development (SATREPS) is a Japanese government program that promotes international joint research. The program is structured as a collaboration between the Japan Science and Technology Agency (JST) and the Japan International Cooperation Agency (JICA). The program includes various fields, such as Environment and Energy, Bioresources, Disaster Prevention and Mitigation, and Infectious Disease Control, and a total 52 projects were currently in progress as of May, 2018. It is expected that the promotion of international joint research under this program will enable Japanese research institutions to conduct research more effectively in fields and having targets that make it advantageous to do that research in developing countries, including countries in Latin America and the Caribbean, Asia, and Africa. Recently, SATREPS projects in the field of Infectious Disease have been but under the control of the Japan Agency for Medical Research and Development (AMED). Although adult maladies, such as malignant tumors, heart disease, and cerebral apoplexy, are major causes of death in the developed countries including Japan, infectious diseases are still responsible for the high mortality rates in developing countries. Therefore, Infectious Disease Control is the important field of SATREPS. Infectious Disease Control projects are progressing in several countries, including Kenya, Zambia, Bangladesh, the Philippines, and Brazil, and various infectious diseases and pathogens have been targeted. In this special issue on Infectious Disease Control, the following reports from three projects have been selected: “The JICA-AMED SATREPS Project to Control Outbreaks of Yellow Fever and Rift Valley Fever in Kenya” by Nagasaki University, “Comprehensive Etiological and Epidemiological Study on Acute Respiratory Infections in Children in the Philippines” by Tohoku University, and “International Joint Research on Antifungal Resistant Fungi in Brazil” by Chiba University. These projects include viral, bacterial, and fungal infections. If they become available, further supplementary reports from other projects in this field will be published in a future issue.

scholarly journals Fusing Node Embeddings and Incomplete Attributes by Complement-Based Concatenation

2021 ◽  
Vol 2021 ◽  
pp. 1-10
Author(s):  
Zheng Wang ◽  
Yuexin Wu ◽  
Yang Bao ◽  
Jing Yu ◽  
Xiaohui Wang
Keyword(s):  
Critical Role ◽  
Inductive Method ◽  
Network Embedding ◽  
Parametric Function ◽  
Adjacency Graph ◽  
Network Nodes ◽  
Learning Tasks ◽  
Multiple Network ◽  
The Given ◽  
Node Embeddings

Network embedding that learns representations of network nodes plays a critical role in network analysis, since it enables many downstream learning tasks. Although various network embedding methods have been proposed, they are mainly designed for a single network scenario. This paper considers a “multiple network” scenario by studying the problem of fusing the node embeddings and incomplete attributes from two different networks. To address this problem, we propose to complement the incomplete attributes, so as to conduct data fusion via concatenation. Specifically, we first propose a simple inductive method, in which attributes are defined as a parametric function of the given node embedding vectors. We then propose its transductive variant by adaptively learning an adjacency graph to approximate the original network structure. Additionally, we also provide a light version of this transductive variant. Experimental results on four datasets demonstrate the superiority of our methods.

International Research Collaborations as Perceived by Top-Performing Scholars

2019 ◽  
pp. 102831531988739
Author(s):  
Miri Yemini
Keyword(s):  
Higher Education ◽  
Social Norms ◽  
International Research ◽  
Scientific Work ◽  
Joint Research ◽  
Research Collaborations ◽  
International Collaborations ◽  
Nuanced Understanding ◽  
Depth Interviews ◽  
Influential Parameters

This study combines two rapidly growing bodies of literature; one addresses the reasons behind the success of highly productive academics and the second investigates collaborations (international coauthorships in particular). The growing literatures on these two topics mainly involve quantitative bibliometric explanatory studies, denoting the demographic, institutional, and national factors as influential parameters that shape these trends. In this study, in contrast, I employ the notion of agency to analyze 20 in-depth interviews with top-producing academics from Denmark, Israel, and Australia in the fields of education and nanoscience, seeking to better understand the motivations, nature, perceived benefits, and drawbacks of such collaborations. I argue that these highly productive scholars involve themselves in international collaborations for a variety of reasons, but mainly due to the potential of such collaborations to advance their research. However, while scholars in nanoscience align with the disciplinary norms of collaborations and see such partnerships as a mundane part of their scientific work, scholars in education (where international collaborations are less common) perceive these activities as agentic, whereby participants often counteract social norms to pursue joint research. In the context of increasing pressures for accountability, commercialization, and internationalization of and in higher education, this study suggests a nuanced understanding of international research collaborations practiced by highly productive scholars.

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