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 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 Power Quality: Scientific Collaboration Networks and Research Trends

Energies ◽  
2018 ◽  
Vol 11 (8) ◽  
pp. 2067 ◽  
Author(s):  
Francisco Montoya ◽  
Raul Baños ◽  
Alfredo Alcayde ◽  
Maria Montoya ◽  
Francisco Manzano-Agugliaro
Keyword(s):  
Power Quality ◽  
Scientific Collaboration ◽  
Research Field ◽  
Research Trends ◽  
Collaboration Network ◽  
Collaboration Networks ◽  
Different Types ◽  
Voltage Frequency ◽  
Advanced Model ◽  
Proper Operation

Power quality is a research field related to the proper operation of devices and technological equipment in industry, service, and domestic activities. The level of power quality is determined by variations in voltage, frequency, and waveforms with respect to reference values. These variations correspond to different types of disturbances, including power fluctuations, interruptions, and transients. Several studies have been focused on analysing power quality issues. However, there is a lack of studies on the analysis of both the trending topics and the scientific collaboration network underlying the field of power quality. To address these aspects, an advanced model is used to retrieve data from publications related to power quality and analyse this information using a graph visualisation software and statistical tools. The results suggest that research interests are mainly focused on the analysis of power quality problems and mitigation techniques. Furthermore, they are observed important collaboration networks between researchers within and across countries.

scholarly journals Scientific collaboration network ammong Brazilian universities

2013 ◽  
Vol 6 (1) ◽  
Author(s):  
Maria Isabel Escalona-Fernandez ◽  
Antonio Pulgarin-Guerrero ◽  
Ely Francina Tannuri de Oliveira ◽  
Maria Cláudia Cabrini Gracio
Keyword(s):  
Social Network ◽  
Social Network Analysis ◽  
Network Analysis ◽  
Scientific Collaboration ◽  
Methodological Approach ◽  
Scientific Production ◽  
Collaboration Network ◽  
Network Data ◽  
New Knowledge

This paper analyses the scientific collaboration network formed by the Brazilian universities that investigate in dentistry area. The constructed network is based on the published documents in the Scopus (Elsevier) database covering a period of 10 (ten) years. It is used social network analysis as the best methodological approach to visualize the capacity for collaboration, dissemination and transmission of new knowledge among universities. Cohesion and density of the collaboration network is analyzed, as well as the centrality of the universities as key-actors and the occurrence of subgroups within the network. Data were analyzed using the software UCINET and NetDraw. The number of documents published by each university was used as an indicator of its scientific production.

scholarly journals Network Embedding via a Bi-Mode and Deep Neural Network Model

2017 ◽  
Author(s):  
Yang Fang ◽  
Xiang Zhao ◽  
Zhen Tan
Keyword(s):  
Neural Network ◽  
Deep Neural Network ◽  
Semantic Information ◽  
Dimensional Space ◽  
Relation Extraction ◽  
Network Embedding ◽  
Structure Information ◽  
Second Mode ◽  
Real World Datasets ◽  
Low Dimensional

Network Embedding (NE) is an important method to learn the representations of network via a low-dimensional space. Conventional NE models focus on capturing the structure information and semantic information of vertices while neglecting such information for edges. In this work, we propose a novel NE model named BimoNet to capture both the structure and semantic information of edges. BimoNet is composed of two parts, i.e., the bi-mode embedding part and the deep neural network part. For bi-mode embedding part, the first mode named add-mode is used to express the entity-shared features of edges and the second mode named subtract-mode is employed to represent the entity-specific features of edges. These features actually reflect the semantic information. For deep neural network part, we firstly regard the edges in a network as nodes, and the vertices as links, which will not change the overall structure of the whole network. Then we take the nodes' adjacent matrix as the input of the deep neural network as it can obtain similar representations for nodes with similar structure. Afterwards, by jointly optimizing the objective function of these two parts, BimoNet could preserve both the semantic and structure information of edges. In experiments, we evaluate BimoNet on three real-world datasets and task of relation extraction, and BimoNet is demonstrated to outperform state-of-the-art baseline models consistently and significantly.

Scientific collaboration network of Turkey

2013 ◽  
Vol 57 ◽  
pp. 9-18 ◽  
Author(s):  
Abdullah Çavuşoğlu ◽  
İlker Türker
Keyword(s):  
Scientific Collaboration ◽  
Collaboration Network

scholarly journals A Strategic View for Rare Earths Production, in a Competitive and Sustainable form

2016 ◽  
Vol 6 (4) ◽  
pp. 140
Author(s):  
Tereza Raquel Taulois Campos ◽  
Marcus Vinicius de Araujo Fonseca ◽  
Bruna de Paula Fonseca e Fonseca ◽  
Edison de Oliveira Martins F
Keyword(s):  
Rare Earths ◽  
Scientific Collaboration ◽  
Web Of Science ◽  
Electronic Equipment ◽  
Collaboration Network ◽  
Scientific Publications ◽  
Industrial Solid Waste ◽  
Alternative Sources ◽  
Institutional Network ◽  
Network Collaboration

The demand for rare earths (RE) has been intensified by their large use, especially in high technology sectors. Supply difficulties have forced RE users to seek alternative sources and invest in the development of recycling technologies and options of reuse for these elements. This article seeks to reveal the trends and ongoing changes in national and global prospects of RE. Additionally, it aims to analyze scientific collaboration networks in the area of industrial solid waste (ISW) and waste electrical and electronic equipment (WEEE) exploitation in Brazil, examining both researchers and institutions with greater representation in the field. For this purpose, social network analysis methods were used to build and analyze co-authorship networks based on scientific publications retrieved from the Web of Science (WoS) database. The results showed that the Brazilian collaboration network of ISW research was extremely fragmented and contained 105 different groups, which were not connected to each other. The institutional network of ISW research was composed of 125 institutions, 75.2% of them from Brazil. The Brazilian collaboration network of research in WEEE was small (37 researchers), but fragmented: researchers were divided into eight different groups that do not connect to each other. The institutional network of research in WEEE was composed by 12 institutions, nine of them from Brazil. Therefore, this article presents a network collaboration model to bring together actors involved in the management of waste electrical and electronic equipment (WEEE), emphasizing the potential for recovery of RE from these wastes, with the purpose of developing products and services. 

scholarly journals Relation Structure-Aware Heterogeneous Information Network Embedding

2019 ◽  
Vol 33 ◽  
pp. 4456-4463 ◽  
Author(s):  
Yuanfu Lu ◽  
Chuan Shi ◽  
Linmei Hu ◽  
Zhiyuan Liu
Keyword(s):  
Real World ◽  
Dimensional Space ◽  
Structural Characteristics ◽  
Information Network ◽  
Network Embedding ◽  
Heterogeneous Information Network ◽  
Heterogeneous Information ◽  
Real World Datasets ◽  
Low Dimensional ◽  
Embedding Methods

Heterogeneous information network (HIN) embedding aims to embed multiple types of nodes into a low-dimensional space. Although most existing HIN embedding methods consider heterogeneous relations in HINs, they usually employ one single model for all relations without distinction, which inevitably restricts the capability of network embedding. In this paper, we take the structural characteristics of heterogeneous relations into consideration and propose a novel Relation structure-aware Heterogeneous Information Network Embedding model (RHINE). By exploring the real-world networks with thorough mathematical analysis, we present two structure-related measures which can consistently distinguish heterogeneous relations into two categories: Affiliation Relations (ARs) and Interaction Relations (IRs). To respect the distinctive characteristics of relations, in our RHINE, we propose different models specifically tailored to handle ARs and IRs, which can better capture the structures and semantics of the networks. At last, we combine and optimize these models in a unified and elegant manner. Extensive experiments on three real-world datasets demonstrate that our model significantly outperforms the state-of-the-art methods in various tasks, including node clustering, link prediction, and node classification.

scholarly journals An Attention-Based Spatiotemporal Gated Recurrent Unit Network for Point-of-Interest Recommendation

2019 ◽  
Vol 8 (8) ◽  
pp. 355 ◽  
Author(s):  
Chunyang Liu ◽  
Jiping Liu ◽  
Jian Wang ◽  
Shenghua Xu ◽  
Houzeng Han ◽  
...  
Keyword(s):  
Behavioral Intention ◽  
Early Stage ◽  
Time Interval ◽  
Time Step ◽  
Attention Model ◽  
Point Of Interest ◽  
Poi Recommendation ◽  
Real World Datasets ◽  
Current Sequence ◽  
Gated Recurrent Unit

Point-of-interest (POI) recommendation is one of the fundamental tasks for location-based social networks (LBSNs). Some existing methods are mostly based on collaborative filtering (CF), Markov chain (MC) and recurrent neural network (RNN). However, it is difficult to capture dynamic user’s preferences using CF based methods. MC based methods suffer from strong independence assumptions. RNN based methods are still in the early stage of incorporating spatiotemporal context information, and the user’s main behavioral intention in the current sequence is not emphasized. To solve these problems, we proposed an attention-based spatiotemporal gated recurrent unit (ATST-GRU) network model for POI recommendation in this paper. We first designed a novel variant of GRU, which acquired the user’s sequential preference and spatiotemporal preference by feeding the continuous geographical distance and time interval information into the GRU network in each time step. Then, we integrated an attention model into our network, which is a personalized process and can capture the user’s main behavioral intention in the user’s check-in history. Moreover, we conducted an extensive performance evaluation on two real-world datasets: Foursquare and Gowalla. The experimental results demonstrated that the proposed ATST-GRU network outperforms the existing state-of-the-art POI recommendation methods significantly regarding two commonly-used evaluation metrics.

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