scholarly journals Graph Neural Networks: Taxonomy, Advances, and Trends

2022 ◽  
Vol 13 (1) ◽  
pp. 1-54
Author(s):  
Yu Zhou ◽  
Haixia Zheng ◽  
Xin Huang ◽  
Shufeng Hao ◽  
Dengao Li ◽  
...  
Keyword(s):  
Neural Networks ◽  
Real World ◽  
Research Community ◽  
Future Research ◽  
Research Directions ◽  
Comprehensive Review ◽  
Future Research Directions ◽  
Graph Neural Networks ◽  
Low Dimensional

Graph neural networks provide a powerful toolkit for embedding real-world graphs into low-dimensional spaces according to specific tasks. Up to now, there have been several surveys on this topic. However, they usually lay emphasis on different angles so that the readers cannot see a panorama of the graph neural networks. This survey aims to overcome this limitation and provide a systematic and comprehensive review on the graph neural networks. First of all, we provide a novel taxonomy for the graph neural networks, and then refer to up to 327 relevant literatures to show the panorama of the graph neural networks. All of them are classified into the corresponding categories. In order to drive the graph neural networks into a new stage, we summarize four future research directions so as to overcome the challenges faced. It is expected that more and more scholars can understand and exploit the graph neural networks and use them in their research community.

MetaLearning with Graph Neural Networks

2021 ◽  
Vol 23 (2) ◽  
pp. 13-22
Author(s):  
Debmalya Mandal ◽  
Sourav Medya ◽  
Brian Uzzi ◽  
Charu Aggarwal
Keyword(s):  
Neural Networks ◽  
Deep Neural Networks ◽  
Future Research ◽  
Open Problems ◽  
Research Directions ◽  
Graph Problems ◽  
Meta Learning ◽  
Comprehensive Survey ◽  
Future Research Directions ◽  
Graph Neural Networks

Graph Neural Networks (GNNs), a generalization of deep neural networks on graph data have been widely used in various domains, ranging from drug discovery to recommender systems. However, GNNs on such applications are limited when there are few available samples. Meta-learning has been an important framework to address the lack of samples in machine learning, and in recent years, researchers have started to apply meta-learning to GNNs. In this work, we provide a comprehensive survey of different metalearning approaches involving GNNs on various graph problems showing the power of using these two approaches together. We categorize the literature based on proposed architectures, shared representations, and applications. Finally, we discuss several exciting future research directions and open problems.

scholarly journals Beyond Intra-modality: A Survey of Heterogeneous Person Re-identification

2020 ◽  
Author(s):  
Zheng Wang ◽  
Zhixiang Wang ◽  
Yinqiang Zheng ◽  
Yang Wu ◽  
Wenjun Zeng ◽  
...  
Keyword(s):  
Video Analysis ◽  
State Of The Art ◽  
Future Research ◽  
Low Resolution ◽  
Research Directions ◽  
Comprehensive Review ◽  
Practical Applications ◽  
Future Research Directions

An efficient and effective person re-identification (ReID) system relieves the users from painful and boring video watching and accelerates the process of video analysis. Recently, with the explosive demands of practical applications, a lot of research efforts have been dedicated to heterogeneous person re-identification (Hetero-ReID). In this paper, we provide a comprehensive review of state-of-the-art Hetero-ReID methods that address the challenge of inter-modality discrepancies. According to the application scenario, we classify the methods into four categories --- low-resolution, infrared, sketch, and text. We begin with an introduction of ReID, and make a comparison between Homogeneous ReID (Homo-ReID) and Hetero-ReID tasks. Then, we describe and compare existing datasets for performing evaluations, and survey the models that have been widely employed in Hetero-ReID. We also summarize and compare the representative approaches from two perspectives, i.e., the application scenario and the learning pipeline. We conclude by a discussion of some future research directions. Follow-up updates are available at https://github.com/lightChaserX/Awesome-Hetero-reID

Artificial Intelligence Review

2019 ◽  
pp. 23-39
Author(s):  
Amal Kilani ◽  
Ahmed Ben Hamida ◽  
Habib Hamam
Keyword(s):  
Artificial Intelligence ◽  
Neural Networks ◽  
Fuzzy Logic ◽  
Intelligent System ◽  
Route Planning ◽  
Turing Test ◽  
Future Research ◽  
Interaction Data ◽  
Research Directions ◽  
Future Research Directions

In this chapter, the authors present a profound literature review of artificial intelligence (AI). After defining it, they briefly cover its history and enumerate its principal fields of application. They name, for example, information system, commerce, image processing, human-computer interaction, data compression, robotics, route planning, etc. Moreover, the test that defines an artificially intelligent system, called the Turing test, is also defined and detailed. Afterwards, the authors describe some AI tools such as fuzzy logic, genetic algorithms, and swarm intelligence. Special attention will be given to neural networks and fuzzy logic. The authors also present the future research directions and ethics.

scholarly journals Privacy Protection and Secondary Use of Health Data: Strategies and Methods

2021 ◽  
Vol 2021 ◽  
pp. 1-11
Author(s):  
Dingyi Xiang ◽  
Wei Cai
Keyword(s):  
Big Data ◽  
Privacy Protection ◽  
Health Data ◽  
Future Research ◽  
Research Directions ◽  
Comprehensive Review ◽  
Secondary Use ◽  
Potential Value ◽  
Preferred Direction ◽  
Future Research Directions

Health big data has already been the most important big data for its serious privacy disclosure concerns and huge potential value of secondary use. Measurements must be taken to balance and compromise both the two serious challenges. One holistic solution or strategy is regarded as the preferred direction, by which the risk of reidentification from records should be kept as low as possible and data be shared with the principle of minimum necessary. In this article, we present a comprehensive review about privacy protection of health data from four aspects: health data, related regulations, three strategies for data sharing, and three types of methods with progressive levels. Finally, we summarize this review and identify future research directions.

scholarly journals Myocardial infarction with non-obstructive coronary arteries: A comprehensive review and future research directions

2019 ◽  
Vol 11 (12) ◽  
pp. 305-315 ◽  
Author(s):  
Rafael Vidal-Perez ◽  
Charigan Abou Jokh Casas ◽  
Rosa Maria Agra-Bermejo ◽  
Belén Alvarez-Alvarez ◽  
Julia Grapsa ◽  
...  
Keyword(s):  
Myocardial Infarction ◽  
Coronary Arteries ◽  
Future Research ◽  
Research Directions ◽  
Comprehensive Review ◽  
Future Research Directions

scholarly journals Automated Machine Learning on Graphs: A Survey

2021 ◽  
Author(s):  
Ziwei Zhang ◽  
Xin Wang ◽  
Wenwu Zhu
Keyword(s):  
Machine Learning ◽  
Learning Algorithm ◽  
Research Community ◽  
Future Research ◽  
Research Directions ◽  
Vast Number ◽  
Neural Architecture ◽  
Future Research Directions ◽  
Automated Machine Learning ◽  
Optimal Machine

Machine learning on graphs has been extensively studied in both academic and industry. However, as the literature on graph learning booms with a vast number of emerging methods and techniques, it becomes increasingly difficult to manually design the optimal machine learning algorithm for different graph-related tasks. To solve this critical challenge, automated machine learning (AutoML) on graphs which combines the strength of graph machine learning and AutoML together, is gaining attention from the research community. Therefore, we comprehensively survey AutoML on graphs in this paper, primarily focusing on hyper-parameter optimization (HPO) and neural architecture search (NAS) for graph machine learning. We further overview libraries related to automated graph machine learning and in-depth discuss AutoGL, the first dedicated open-source library for AutoML on graphs. In the end, we share our insights on future research directions for automated graph machine learning. This paper is the first systematic and comprehensive review of automated machine learning on graphs to the best of our knowledge.

Ethical, Cultural and Socio- Economic Factors of Software Piracy Determinants in a Developing Country

2009 ◽  
pp. 354-372
Author(s):  
Arsalan Butt
Keyword(s):  
University Students ◽  
Cultural Values ◽  
Software Industry ◽  
Empirical Studies ◽  
Software Piracy ◽  
Research Community ◽  
Future Research ◽  
Research Directions ◽  
The World ◽  
Future Research Directions

Consumer software piracy is widespread in many parts of the world. P2P based websites have made it easier to access pirated software, which has resulted in an increased emphasis on the issue of software piracy in both the software industry and research community. Some factors that determine piracy include poverty, cultural values, ethical attitudes, and education. Earlier empirical studies have looked at software piracy as an intentional behaviour. This study explores the demographic, ethical and socioeconomical factors that can represent software piracy as a social norm among a developing country’s university students. The authors have conducted a comparative analysis of university students from Pakistan and Canada, two countries that differ economically, socially, and culturally. The results of the study indicate that software piracy behaviour is different in both groups of students, but that there are also some similarities. Future research directions and implications are also presented.

Flying Adhoc Networks Concept and Challenges

2019 ◽  
pp. 903-911 ◽  
Author(s):  
Kuldeep Singh ◽  
Anil Kumar Verma
Keyword(s):  
Family Member ◽  
Significant Role ◽  
Research Community ◽  
Future Research ◽  
Research Directions ◽  
Mobile Adhoc Networks ◽  
New Family ◽  
War Zone ◽  
Future Research Directions ◽  
Adhoc Networks

Flying adhoc networks (FANETs) are getting popular among the research community due to their wide area of applications in civilians and tactical areas. FANETs are the new family member of the mobile adhoc networks (MANETs) class. Situations such as flooding, war zone, and rescue operations where traditional MANETs cannot be deploy because they used ground moving nodes. FANETs can play significant role in those situations because they employ a swarm of UAVs to form adhoc network. In this chapter, FANET concept along with its applications and challenges is discussed. Future research directions in the area of FANETs are discussed.

Sport Environment/Atmospherics

2014 ◽  
pp. 104-132 ◽  
Author(s):  
Kelly Price ◽  
Mauro Palmero
Keyword(s):  
Consumer Choice ◽  
Sport Marketing ◽  
Purchase Behavior ◽  
Future Research ◽  
Research Directions ◽  
Comprehensive Review ◽  
Sport Industry ◽  
Future Research Directions ◽  
The Spectator ◽  
Atmospheric Variables

This chapter discusses atmospherics as a sport marketing strategy. Even though it has traditional retail roots, atmospherics have emerged as a strategy that may be utilized in the physical, online, and mobile sport environments. A comprehensive review of major traditional and sports atmospheric variables, online atmospheric variables, and applications to sport are discussed. In addition, the spectator experience cycle is introduced with atmospheric correlations. The purpose of the chapter is to explain why atmospherics are important to the sport industry and to demonstrate how sport marketers may use physical, online, or mobile atmospherics to enhance spectator experience, increase loyalty, impact attitude, consumer choice, and impact purchase behavior. In addition, the chapter is meant to emphasize the importance of atmospherics to ultimately achieve promotional and marketing objectives. Finally, future research directions are recommended.

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