scholarly journals A Systematic Survey on Deep Generative Models for Graph Generation

2020 ◽  
Author(s):  
Xiaojie Guo ◽  
Liang Zhao
Keyword(s):  
Generative Models ◽  
Future Research ◽  
Specific Domain ◽  
Research Directions ◽  
Wide Range ◽  
Data Representations ◽  
Future Research Directions ◽  
Definition Of ◽  
Rich History ◽  
Graph Generation

Graphs are important data representations for describing objects and their relationships, which appear in a wide diversity of real-world scenarios. As one of a critical problem in this area, graph generation considers learning the distributions of given graphs and generating more novel graphs. Owing to its wide range of applications, generative models for graphs have a rich history, which, however, are traditionally hand-crafted and only capable of modeling a few statistical properties of graphs. Recent advances in deep generative models for graph generation is an important step towards improving the fidelity of generated graphs and paves the way for new kinds of applications. This article provides an extensive overview of the literature in the field of deep generative models for graph generation. Firstly, the formal definition of deep generative models for the graph generation as well as preliminary knowledge is provided. Secondly, two taxonomies of deep generative models for unconditional, and conditional graph generation respectively are proposed; the existing works of each are compared and analyzed. After that, an overview of the evaluation metrics in this specific domain is provided. Finally, the applications that deep graph generation enables are summarized and five promising future research directions are highlighted.

scholarly journals A Systematic Survey on Deep Generative Models for Graph Generation

2020 ◽  
Author(s):  
Xiaojie Guo ◽  
Liang Zhao
Keyword(s):  
Generative Models ◽  
Future Research ◽  
Specific Domain ◽  
Research Directions ◽  
Wide Range ◽  
Data Representations ◽  
Future Research Directions ◽  
Definition Of ◽  
Rich History ◽  
Graph Generation

Graphs are important data representations for describing objects and their relationships, which appear in a wide diversity of real-world scenarios. As one of a critical problem in this area, graph generation considers learning the distributions of given graphs and generating more novel graphs. Owing to its wide range of applications, generative models for graphs have a rich history, which, however, are traditionally hand-crafted and only capable of modeling a few statistical properties of graphs. Recent advances in deep generative models for graph generation is an important step towards improving the fidelity of generated graphs and paves the way for new kinds of applications. This article provides an extensive overview of the literature in the field of deep generative models for graph generation. Firstly, the formal definition of deep generative models for the graph generation as well as preliminary knowledge is provided. Secondly, two taxonomies of deep generative models for unconditional, and conditional graph generation respectively are proposed; the existing works of each are compared and analyzed. After that, an overview of the evaluation metrics in this specific domain is provided. Finally, the applications that deep graph generation enables are summarized and five promising future research directions are highlighted.

Positive Workplaces

2009 ◽  
pp. 578-588
Author(s):  
Fred Luthans ◽  
Carolyn M. Youssef
Keyword(s):  
Organizational Behavior ◽  
Psychological Capital ◽  
Future Research ◽  
Positive Organizational Behavior ◽  
Performance Impact ◽  
Research Directions ◽  
Future Research Directions ◽  
And Performance ◽  
Definition Of ◽  
Psychological Capacities

Over the years, both management practitioners and academics have generally assumed that positive workplaces lead to desired outcomes. Unlike psychology, considerable attention has also been devoted to the study of positive topics such as job satisfaction and organizational commitment. However, to place a scientifically based focus on the role that positivity may play in the development and performance of human resources, and largely stimulated by the positive psychology initiative, positive organizational behavior (POB) and psychological capital (PsyCap) have recently been introduced into the management literature. This chapter first provides an overview of both the historical and contemporary positive approaches to the workplace. Then, more specific attention is given to the meaning and domain of POB and PsyCap. Our definition of POB includes positive psychological capacities or resources that can be validly measured, developed, and have performance impact. The constructs that have been determined so far to best meet these criteria are efficacy, hope, optimism, and resiliency. When combined, they have been demonstrated to form the core construct of what we term psychological capital (PsyCap). A measure of PsyCap is being validated and this chapter references the increasing number of studies indicating that PsyCap can be developed and have performance impact. The chapter concludes with important future research directions that can help better understand and build positive workplaces to meet current and looming challenges.

scholarly journals A matrix-less method to approximate the spectrum and the spectral function of Toeplitz matrices with real eigenvalues

2021 ◽  
Author(s):  
Sven-Erik Ekström ◽  
Paris Vassalos
Keyword(s):  
Distribution Function ◽  
Asymptotic Distribution ◽  
Numerical Experiments ◽  
Toeplitz Matrices ◽  
Future Research ◽  
Research Directions ◽  
Numerical Examples ◽  
Working Hypothesis ◽  
Wide Range ◽  
Future Research Directions

AbstractIt is known that the generating function f of a sequence of Toeplitz matrices {Tn(f)}n may not describe the asymptotic distribution of the eigenvalues of Tn(f) if f is not real. In this paper, we assume as a working hypothesis that, if the eigenvalues of Tn(f) are real for all n, then they admit an asymptotic expansion of the same type as considered in previous works, where the first function, called the eigenvalue symbol $\mathfrak {f}$ f , appearing in this expansion is real and describes the asymptotic distribution of the eigenvalues of Tn(f). This eigenvalue symbol $\mathfrak {f}$ f is in general not known in closed form. After validating this working hypothesis through a number of numerical experiments, we propose a matrix-less algorithm in order to approximate the eigenvalue distribution function $\mathfrak {f}$ f . The proposed algorithm, which opposed to previous versions, does not need any information about neither f nor $\mathfrak {f}$ f is tested on a wide range of numerical examples; in some cases, we are even able to find the analytical expression of $\mathfrak {f}$ f . Future research directions are outlined at the end of the paper.

Social media and consumer engagement: a review and research agenda

2016 ◽  
Vol 10 (4) ◽  
pp. 268-287 ◽  
Author(s):  
Victor Barger ◽  
James W. Peltier ◽  
Don E. Schultz
Keyword(s):  
Social Media ◽  
Recent Literature ◽  
Future Research ◽  
Consumer Engagement ◽  
Research Directions ◽  
Content Type ◽  
Future Research Directions ◽  
Antecedents And Consequences ◽  
Area Of Concern ◽  
Definition Of

Purpose In “Social media’s slippery slope: challenges, opportunities and future research directions”, Schultz and Peltier (2013) asked “whether or how social media can be used to leverage consumer engagement into highly profitable relationships for both parties”. The purpose of this article is to continue this discussion by reviewing recent literature on consumer engagement and proposing a framework for future research. Design/methodology/approach The paper reviews the marketing literature on social media, paying particular attention to consumer engagement, which was identified as a primary area of concern in Schultz and Peltier (2013). Findings A significant amount of research has been conducted on consumer engagement since 2010. Lack of consensus on the definition of the construct has led to fragmentation in the discipline, however. As a result, research related to consumer engagement is often not identified as such, making it difficult for academics and practitioners to stay abreast of developments in this area. Originality/value This critical review provides marketing academics and practitioners insights into the antecedents and consequences of consumer engagement and offers a conceptual framework for future research.

Deepfake videos: synthesis and detection techniques –a survey

2021 ◽  
pp. 1-21
Author(s):  
Shahela Saif ◽  
Samabia Tehseen
Keyword(s):  
Social Systems ◽  
Generative Models ◽  
Detection Methods ◽  
Future Research ◽  
Fake News ◽  
Research Directions ◽  
Detection Techniques ◽  
Future Research Directions ◽  
Video Creation ◽  
The One

Deep learning has been used in computer vision to accomplish many tasks that were previously considered too complex or resource-intensive to be feasible. One remarkable application is the creation of deepfakes. Deepfake images change or manipulate a person’s face to give a different expression or identity by using generative models. Deepfakes applied to videos can change the facial expressions in a manner to associate a different speech with a person than the one originally given. Deepfake videos pose a serious threat to legal, political, and social systems as they can destroy the integrity of a person. Research solutions are being designed for the detection of such deepfake content to preserve privacy and combat fake news. This study details the existing deepfake video creation techniques and provides an overview of the deepfake datasets that are publicly available. More importantly, we provide an overview of the deepfake detection methods, along with a discussion on the issues, challenges, and future research directions. The study aims to present an all-inclusive overview of deepfakes by providing insights into the deepfake creation techniques and the latest detection methods, facilitating the development of a robust and effective deepfake detection solution.

scholarly journals CubeSat Communications: Recent Advances and Future Challenges

2020 ◽  
Author(s):  
Nasir Saeed ◽  
Ahmed Elzanaty ◽  
Heba Almorad ◽  
Hayssam Dahrouj ◽  
Tareq Y. Al-Naffouri ◽  
...  
Keyword(s):  
Channel Modeling ◽  
Future Research ◽  
New Paradigm ◽  
Earth Remote Sensing ◽  
Research Directions ◽  
Space Industry ◽  
Recent Advances ◽  
Wide Range ◽  
Future Challenges ◽  
Future Research Directions

<pre><pre>Given the increasing number of space-related applications, research in the emerging space industry is becoming more and more attractive. One compelling area of current space research is the design of miniaturized satellites, known as CubeSats, which are enticing because of their numerous applications and low design-and-deployment cost. </pre><pre>The new paradigm of connected space through CubeSats makes possible a wide range of applications, such as Earth remote sensing, space exploration, and rural connectivity.</pre><pre>CubeSats further provide a complementary connectivity solution to the pervasive Internet of Things (IoT) networks, leading to a globally connected cyber-physical system.</pre><pre>This paper presents a holistic overview of various aspects of CubeSat missions and provides a thorough review of the topic from both academic and industrial perspectives.</pre><pre>We further present recent advances in the area of CubeSat communications, with an emphasis on constellation-and-coverage issues, channel modeling, modulation and coding, and networking.</pre><pre>Finally, we identify several future research directions for CubeSat communications, including Internet of space things, low-power long-range networks, and machine learning for CubeSat resource allocation.</pre></pre>

The Gamification Experience

2014 ◽  
pp. 205-223 ◽  
Author(s):  
Cathie Marache-Francisco ◽  
Eric Brangier
Keyword(s):  
Human Computer Interaction ◽  
User Experience ◽  
Future Research ◽  
Experience Design ◽  
Research Directions ◽  
User Experience Design ◽  
Future Research Directions ◽  
Different Dimensions ◽  
Definition Of ◽  
Game Elements

Through this chapter, the authors aim at describing Gamification—the use of game elements in non-ludic environments—to identify its limits and lacks as well as its assets. Indeed, it has been developed to answer a need that arouses out of the Human Computer Interaction (HCI) field evolutions, and it could be valuable in that scope. The authors propose a definition of Gamification according to several different dimensions that are part of the HCI design field. They suggest it as a first step towards a guiding design framework aimed at designers. They mention future research directions that would help in going further and enriching the framework, leading to the creation of a design model for user experience design through Gamification. The authors finally raise some ethical concerns about the meaning of Gamification itself.

Modern Downsizing

2021 ◽  
pp. 1970-1983
Author(s):  
Grzegorz Wojtkowiak
Keyword(s):  
Financial Analysis ◽  
Management Tool ◽  
Future Research ◽  
Research Directions ◽  
Good Practices ◽  
Strategic Option ◽  
Wide Range ◽  
Future Research Directions ◽  
Points Of View

The aim of the chapter is to present the concept of downsizing from different points of view: as a strategic option, as a management tool and as a phenomenon. It describes the evolution of the term, its definitions, and different directions of development. A scale and possible outcomes are described on the basis of financial analysis; however it also discusses the role of non-financial aspects. The chapter points out reasons, aims and a wide range of tools that may be used during implementation of downsizing. One of the conclusions of the chapter is to present future research directions aiming at increasing knowledge of managers and providing them with detailed good practices.

On the Road to Ephesus

2015 ◽  
pp. 102-125
Author(s):  
Suzanne Roff-Wexler
Keyword(s):  
Big Data ◽  
Future Research ◽  
Review Of Literature ◽  
Healthcare Organizations ◽  
Research Directions ◽  
The Road ◽  
Future Research Directions ◽  
On The Road ◽  
Definition Of

Following a brief review of literature on big data as well as wisdom, this chapter provides a definition of data-based wisdom in the context of healthcare organizations and their visions. The author addresses barriers and ways to overcome barriers to data-based wisdom. Insights from interviews with leading healthcare professionals add practical meaning to the discussion. Finally, future research directions and questions are suggested, including the role of synchronicity and serendipity in data-based wisdom. In this chapter, developing data-based wisdom systems that flourish Wisdom, Virtue, Intellect, and Knowledge are encouraged.

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