Twenty Years of Network Science: A Bibliographic and Co-authorship Network Analysis

AbstractUnderstanding facial signals in humans and other species is crucial for understanding the evolution, complexity, and function of the face as a communication tool. The Facial Action Coding System (FACS) enables researchers to measure facial movements accurately, but we currently lack tools to reliably analyse data and efficiently communicate results. Network analysis can provide a way to use the information encoded in FACS datasets: by treating individual AUs (the smallest units of facial movements) as nodes in a network and their co-occurrence as connections, we can analyse and visualise differences in the use of combinations of AUs in different conditions. Here, we present ‘NetFACS’, a statistical package that uses occurrence probabilities and resampling methods to answer questions about the use of AUs, AU combinations, and the facial communication system as a whole in humans and non-human animals. Using highly stereotyped facial signals as an example, we illustrate some of the current functionalities of NetFACS. We show that very few AUs are specific to certain stereotypical contexts; that AUs are not used independently from each other; that graph-level properties of stereotypical signals differ; and that clusters of AUs allow us to reconstruct facial signals, even when blind to the underlying conditions. The flexibility and widespread use of network analysis allows us to move away from studying facial signals as stereotyped expressions, and towards a dynamic and differentiated approach to facial communication.

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An investigation into colour combination in paintings via graph theory

Journal of Complex Networks ◽

10.1093/comnet/cnz034 ◽

2019 ◽

Vol 8 (4) ◽

Author(s):

Samaneh Jolany Vangah ◽

Yousef Jamali ◽

Mozaffar Jamali

Keyword(s):

Image Processing ◽

Graph Theory ◽

Network Analysis ◽

Visual Arts ◽

Digital Image ◽

Degree Distribution ◽

Network Science ◽

Node Centrality ◽

Classification Of Images

Abstract In visual arts, painting is deeply reliant on the colour combination for its impact, depth and emotion. Recently, many studies have focused on image processing, regarding identification and classification of images, using some colour features such as saturation, hue, luminance and so forth. This study aims to delve into some of the painting styles from the perspective of graph theory and network science. We compared a number of famous paintings to find out the likely pattern that an artist uses for colour combination and juxtaposition. To achieve this aim, the digital image of a painting is converted to a graph where each vertex represents one of the painting’s colours. In this graph, two vertices would be adjacent if and only if the two relative colours could be found in at least two adjacent pixels in the digital image. Among the several tools for network analysis, clustering, node centrality and degree distribution are used. Outcomes showed that artists unconsciously are following subtle mathematical rules to reach harmony and coordination in their work.

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Network Science and International Relations

Oxford Research Encyclopedia of Politics ◽

10.1093/acrefore/9780190228637.013.517 ◽

2017 ◽

Author(s):

Zeev Maoz

Keyword(s):

Network Analysis ◽

International Relations ◽

Network Effects ◽

Network Science ◽

International Politics ◽

Levels Of Analysis ◽

Emergent Properties ◽

Units Of Analysis ◽

Shed Light ◽

Level Of Analysis

Network analysis has been one of the fastest-growing approaches to the study of politics in general and the study of international politics in particular. Network analysis relies on several key assumptions: (a) relations are interdependent, (b) complex relations give rise to emergent and unintended structures, (c) agents’ choices affect structure and structure affects agents’ choices, and (d) once we understand the emergent properties of a system and the interrelations between agents and structure, we can generalize across levels of analysis. These assumptions parallel many of the key features of international relations. Key contributions of network analysis helps shed light on important puzzles in the study and research of international relations. Specifically, (a) network analytic studies helped refine many key concepts and measures of various aspects of international politics; (b) network analysis helped unpack structures of interdependence, uncovering endogenous network effects that have caused biased inferences of dyadic behavior; (c) network analytic studies have shed light on important aspects of emergent structures and previously unrealized units of analysis (e.g., endogenous groups); and (d) network analytic studies helped resolve multiple puzzles, wherein results found at one level of analysis contradicted those found at other levels of analysis.

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Core Kernels for Complex Network Analysis

Advances in Computer and Electrical Engineering - Creativity in Load-Balance Schemes for Multi/Many-Core Heterogeneous Graph Computing ◽

10.4018/978-1-5225-3799-1.ch002 ◽

2018 ◽

pp. 30-58

Keyword(s):

Network Analysis ◽

Complex Networks ◽

Network Science ◽

Efficient Algorithms ◽

The Other ◽

Software Systems ◽

Layer By Layer ◽

Graph Visualization ◽

Graph Problems ◽

Centrality Metrics

The emergence of Network Science has motivated a renewed interest in classical graph problems for the analysis of the topology of complex networks. For example, important centrality metrics, such as the betweenness, the stress, the eccentricity, and the closeness centralities, are all based on BFS. On the other hand, the k-core decomposition of graphs defines a hierarchy of internal cores and decomposes large networks layer by layer. The k-core decomposition has been successfully applied in a variety of domains, including large graph visualization and fingerprinting, analysis of large software systems, and fraud detection. In this chapter, the authors review known efficient algorithms for traversing and decomposing large complex networks and provide insights on how the decomposition of graphs in k-cores can be useful for developing novel topology-aware algorithms.

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Geographies of transport II

Progress in Human Geography ◽

10.1177/0309132516628259 ◽

2016 ◽

Vol 41 (3) ◽

pp. 355-364 ◽

Cited By ~ 29

Author(s):

Tim Schwanen

Keyword(s):

Big Data ◽

Network Analysis ◽

Complex Networks ◽

Network Science ◽

Complexity Science ◽

Transport Systems ◽

Transport Geography ◽

Space And Time ◽

Critical Reflexivity ◽

Mixed Blessing

Geographical scholarship on transport has been boosted by the emergence of big data and advances in the analysis of complex networks in other disciplines, but these developments are a mixed blessing. They allow transport as object of analysis to exist in new ways and raise the profile of geography in interdisciplinary spaces dominated by physics and complexity science. Yet, they have also brought back concerns over the privileging of generality over particularity. This is because they have once more made acceptable and even normalized a focus on supposedly universal laws that explain the functioning of mobility systems and on space and time independent explanations of hierarchies, inequalities and vulnerabilities in transport systems and patterns. Geographical scholarship on transport should remain open to developments in big data and network science but would benefit from more critical reflexivity on the limitations and the historical and geographical situatedness of big data and on the conceptual shortcomings of network science. Big data and network analysis need to be critiqued and re-appropriated, and examples of how this can be done are starting to emerge. Openness, critique and re-appropriation are especially important in a context where transport geography decentralizes away from its Euro-American core, and the development pathways of transport and mobility in localities beyond that core deserve their own, unique explanations.

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Dynamics in and dynamics of networks using DyNSimF

10.31234/osf.io/6kcfz ◽

2021 ◽

Author(s):

Maarten van den Ende ◽

Mathijs Mayer ◽

Sacha Epskamp ◽

Michael Lees ◽

Han van der Maas

Keyword(s):

Network Analysis ◽

Data Collection ◽

Open Source ◽

Network Science ◽

Complexity Science ◽

Formal Theories

Advancements of formal theories, network science, and data collection technologies make network analysis and simulation an increasingly crucial tool in complexity science. We present DyNSimF; the first open-source package that allows for the modeling of com- plex interacting dynamics on a network a well as dynamics of (the structure of) a net- work. The package can deal with weighted as well as directional connections, is scalable and efficient, and includes a utility-based edge-altering framework. DyNSimF includes visualization methods and tools to help analyze models. It is designed to be easily ex- tendable and makes use of NetworkX graphs. It aims to be easy to learn and to work with, enabling non-experts to focus on the development of models, while at the same time being highly customizable and extensible to allow for complex custom models.

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Complexity of Tourism Destination Governance

10.4018/978-1-6684-3889-3.ch008 ◽

2022 ◽

pp. 119-132

Author(s):

Tomáš Gajdošík ◽

Marco Valeri

Keyword(s):

Network Analysis ◽

Network Science ◽

Network Approach ◽

Tourism Destination ◽

Case Study Methodology ◽

Limited Power ◽

Study Methodology ◽

Tourism Destinations ◽

Significant Attention

Tourism destinations can be considered as complex systems of interrelated and interdependent stakeholders. The complexity and limited power of influencing the number of stakeholders resulted in network approach to tourism destination governance. This approach is considered both theoretically and practically as a tool for strengthening its sustainable competitiveness, fostering innovation and knowledge sharing. Although the network analysis of tourism destinations has gained a significant attention in recent years, the complex understanding of its contribution to smart development is still missing. The aim of this chapter is to create a framework for smart approach in destination governance using the network science perspective. The chapter provides insights in using network analysis for strengthening the tourism destination governance. The chapter uses a case study methodology on two mature tourism destinations, providing an example of the use of network analysis for destination governance strengthening.

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Network science and sustainable performance of family businesses in tourism

Journal of Family Business Management ◽

10.1108/jfbm-06-2020-0048 ◽

2020 ◽

Vol ahead-of-print (ahead-of-print) ◽

Author(s):

Rodolfo Baggio ◽

Marco Valeri

Keyword(s):

Network Analysis ◽

Network Science ◽

Qualitative Content Analysis ◽

Research Work ◽

Family Businesses ◽

Future Research ◽

Content Type ◽

Sustainable Performance ◽

Managerial Implications ◽

Tourism And Hospitality

PurposeThere is a little appreciation for the role network science can play in sustainable tourism, and it is not quite clear to what extent generic models from the tourism network analysis literature are applicable. In the international management literature, then, few significant studies exist that deal with the effects of network structures on the sustainable performance of tourism family businesses. This research analyzes these issues and discusses the state of the art of this area.Design/methodology/approachBased on a scrutiny of the literature conducted on research papers published in the last twenty years, this analysis focuses on the relation between network analysis methods and sustainable performance within the tourism family business domain. The paper uses a limited set of keywords, restricting the selection to tourism and hospitality works on sustainability. A qualitative content analysis is performed.FindingsThe results suggest a critical reflection on how the methods of network science can be profitably and advantageously used for supporting a sustainable performance of family businesses in tourism.Originality/valueThe paper contains a critical consideration on the potential drivers and drawbacks of the relationship between sustainability and networking in tourism and highlights some managerial implications. The analysis closes with some suggestions and indications for future research work.

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CINNA: An R/CRAN package to decipher Central Informative Nodes in Network Analysis

10.1101/168757 ◽

2017 ◽

Cited By ~ 1

Author(s):

Minoo Ashtiani ◽

Mehdi Mirzaie ◽

Mohieddin Jafari

Keyword(s):

Network Analysis ◽

Network Structure ◽

Dimensional Reduction ◽

Network Science ◽

The Other ◽

Centrality Measures ◽

Large Network ◽

Topological Features ◽

Reduction Methods ◽

Centrality Analysis

AbstractIn network science, usually there is a critical step known as centrality analysis. This is an important step, since by using centrality measures, a large number of vertices with low priority are set aside and only a few ones remain to be used for further inferential outcomes. In the other words, these measures help us to sieve our large network and distinguish coarse vertices. By that, important decisions could be made based on the circumstances of these vertices on the overall behavior of networks. These vertices are potentially assumed as central or essential nodes. However, the centrality analysis has always been accompanied by a series of ambiguities, since there are a large number of well-known centrality measures, with different algorithms pointing to these essential nodes and there is no well-defined preference. Which measure explore more information in a given network about node essentiality according to the topological features? While here, we tried to provide a pipeline to have a comparison among all proper centrality measures regarding the network structure and choose the most informative one according to dimensional reduction methods. Central Informative Nodes in Network Analysis (CINNA) package is prepared to gather all required function for centrality analysis in the weighted/unweighted and directed/undirected networks.Availability and implementationCINNA is available in CRAN, including a tutorial. URL: https://cran.r-proiect.org/web/packages/CINNA/index.htmlContact:[email protected]

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Football is becoming more predictable; network analysis of 88 thousand matches in 11 major leagues

Royal Society Open Science ◽

10.1098/rsos.210617 ◽

2021 ◽

Vol 8 (12) ◽

Author(s):

Victor Martins Maimone ◽

Taha Yasseri

Keyword(s):

Network Analysis ◽

Network Science ◽

Direct Analysis ◽

High Income ◽

The Other ◽

Science Approach ◽

Home Field Advantage ◽

The One ◽

Over Time

In recent years, excessive monetization of football and professionalism among the players have been argued to have affected the quality of the match in different ways. On the one hand, playing football has become a high-income profession and the players are highly motivated; on the other hand, stronger teams have higher incomes and therefore afford better players leading to an even stronger appearance in tournaments that can make the game more imbalanced and hence predictable. To quantify and document this observation, in this work, we take a minimalist network science approach to measure the predictability of football over 26 years in major European leagues. We show that over time, the games in major leagues have indeed become more predictable. We provide further support for this observation by showing that inequality between teams has increased and the home-field advantage has been vanishing ubiquitously. We do not include any direct analysis on the effects of monetization on football’s predictability or therefore, lack of excitement; however, we propose several hypotheses which could be tested in future analyses.

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