The Dynamic Network Evolution of C2 Communications over Time

The examination of moment-to-moment, ‘micro-level’ patterns of experience and behaviour using experience sampling methodology has contributed to our understanding of the ‘macro-level’ development of full-blown symptoms and disorders. This paper argues that the micro-level perspective can be used to identify the smallest building blocks underlying the onset and course of mental ill-health. Psychopathology may be the result of the continuous dynamic interplay between micro-level moment-to-moment experiences and behavioural patterns over time. Reinforcing loops between momentary states may alter the course of mental health towards either a more or less healthy state. An example with observed data, from a population of individuals with depressive symptoms, supports the validity of a dynamic network model of psychopathology and shows that together and over time, this continuous interplay between momentary states may result in the cluster of symptoms we call major depressive disorder. This approach may help conceptualize the nature of mental disorders, and generate individualized insights useful for diagnosis and treatment in psychiatry.

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Emerging, crystalizing, and changing psychological contracts over time: Introducing the iPC-network model

10.31234/osf.io/8ydmf ◽

2019 ◽

Author(s):

Tim Vantilborgh

Keyword(s):

Network Model ◽

Psychological Contract ◽

Dynamic Network ◽

Network Models ◽

Psychological Contracts ◽

Exchange Relationships ◽

Contract Breach ◽

Alternative Approach ◽

The Individual ◽

Over Time

This chapter introduces the individual Psychological Contract (iPC) network model as an alternative approach to study psychological contracts. This model departs from the basic idea that a psychological contract forms a mental schema containing obligated inducements and contributions, which are exchanged for each other. This mental schema is captured by a dynamic network, in which the nodes represent the inducements and contributions and the ties represent the exchanges. Building on dynamic systems theory, I propose that these networks evolve over time towards attractor states, both at the level of the network structure and at the level of the nodes (i.e., breach and fulfilment attractor states). I highlight how the iPC-network model integrates recent theoretical developments in the psychological contract literature and explain how it may advance scholars understanding of exchange relationships. In particular, I illustrate how iPC-network models allow researchers to study the actual exchanges in the psychological contract over time, while acknowledging its idiosyncratic nature. This would allow for more precise predictions of psychological contract breach and fulfilment consequences and explains how content and process of the psychological contract continuously influence each other.

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Dynamics and Evolutional Patterns of Social Networks

Social Network Mining, Analysis, and Research Trends ◽

10.4018/978-1-61350-513-7.ch010 ◽

2011 ◽

pp. 156-170

Author(s):

Yingzi Jin ◽

Yutaka Matsuo

Keyword(s):

Social Networks ◽

Social Network ◽

Network Analysis ◽

Real World ◽

Dynamic Network ◽

Network Evolution ◽

Belief Formation ◽

Network Mining ◽

Longitudinal Network Analysis ◽

Relational Network

Previous chapters focused on the models of static networks, which consider a relational network at a given point in time. However, real-world social networks are dynamic in nature; for example, friends of friends become friends. Social network research has, in recent years, paid increasing attention to dynamic and longitudinal network analysis in order to understand network evolution, belief formation, friendship formation, and so on. This chapter focuses mainly on the dynamics and evolutional patterns of social networks. The chapter introduces real-world applications and reviews major theories and models of dynamic network mining.

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Evolution of Trust and Formation of Preference Clusters in Distributed Networked Structure

International Journal of Virtual Communities and Social Networking ◽

10.4018/jvcsn.2011040102 ◽

2011 ◽

Vol 3 (2) ◽

pp. 17-50 ◽

Cited By ~ 2

Author(s):

Purnendu Karmakar ◽

Rajarshi Roy

Keyword(s):

Social Science ◽

Trust Model ◽

Network Evolution ◽

Human Interaction ◽

Distributed Network ◽

Chat Rooms ◽

Interacting Agents ◽

Over Time

Distributed network researchers are trying to address one important issue concerning networked structures and how the network came into existence, i.e., dynamics of network evolution. From the knowledge of social science it is observed that trust is one such metric that evolves with the network particularly where human interaction is involved. This work presents a “trust” model of the authors’ studies and its various properties. In virtual (and “real”) communities (chat rooms, blogs, etc.) behavioral segregation over time is observed. Differences in identities of interacting agents result in evolution of various degrees of “trust” (and “distrust’) among them over a period of time. This process ultimately leads to emergence of self-segregation in behavioral kinetics and results in formation of preference clusters.

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Unpacking Changing Multi-Actor and Multi-Level Actor Ties in Transformative Spaces: Insights from a Degraded Landscape, Machubeni, South Africa

Land ◽

10.3390/land9070227 ◽

2020 ◽

Vol 9 (7) ◽

pp. 227

Author(s):

Menelisi Falayi ◽

James Gambiza ◽

Michael Schoon

Keyword(s):

South Africa ◽

Collective Action ◽

Land Degradation ◽

Network Evolution ◽

Clustering Coefficient ◽

Edge Density ◽

Policy Outcomes ◽

Local Municipality ◽

Multi Level ◽

Over Time

The loss of ecosystem services through land degradation continues to be a significant concern for policymakers and land users around the world. Facilitating collective action among various actors is regarded as imperative in halting land degradation. Despite extensive research on collective action, there have been few studies that continuously map social ties and detect network evolution as a way of enabling longitudinal analysis of transformative spaces. This paper seeks to examine the changing dynamics of multi-actor and multi-level actor ties over a period of two years in Machubeni, South Africa. To do this, we used social network analysis to detect continuities and/or discontinuities of multi-actor and multi-level actor ties over time. Overall, edge density, clustering coefficient, and reciprocity scores steadily increased over the two years despite a decline in the number of active organisations within the network. Our results demonstrate that the proportion of strong ties gradually increased over time across three governance networks. However, multi-level linkages between the local municipality and the local organisations remained weak due to a lack of trust and collaborative fatigue. While the transformative space has succeeded in enhancing collaboration and knowledge sharing between local organisations and researchers, further long-term engagement with government agencies might be necessary for promoting institutional transformations and policy outcomes, and building network resilience in complex polycentric governance systems.

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The evolution of political networks: Evidence from the Council of the European Union

European Union Politics ◽

10.1177/1465116517740228 ◽

2017 ◽

Vol 19 (1) ◽

pp. 25-51 ◽

Cited By ~ 4

Author(s):

Narisong Huhe ◽

Daniel Naurin ◽

Robert Thomson

Keyword(s):

Decision Making ◽

European Union ◽

Network Theory ◽

Network Evolution ◽

The European Union ◽

Unique Combination ◽

Policy Positions ◽

Political Actors ◽

Political Networks ◽

Over Time

We test two of the main explanations of the formation of political ties. The first states that political actors are more likely to form a relationship if they have similar policy preferences. The second explanation, from network theory, predicts that the likelihood of a tie between two actors depends on the presence of certain relationships with other actors. Our data consist of a unique combination of actors' policy positions and their network relations over time in the Council of the European Union. We find evidence that both types of explanations matter, although there seems to be variation in the extent to which preference similarity affects network evolution. We consider the implications of these findings for understanding the decision-making in the Council.

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Evolution of communities of software: using tensor decompositions to compare software ecosystems

Applied Network Science ◽

10.1007/s41109-019-0193-5 ◽

2019 ◽

Vol 4 (1) ◽

Author(s):

Oliver A. Blanthorn ◽

Colin M. Caine ◽

Eva M. Navarro-López

Keyword(s):

Dynamic Network ◽

Tensor Decomposition ◽

Software Projects ◽

R Software ◽

Tensor Decompositions ◽

Software Ecosystems ◽

Software Libraries ◽

Dependency Networks ◽

Software Packages ◽

Over Time

AbstractModern software development is often a collaborative effort involving many authors through the re-use and sharing of code through software libraries. Modern software “ecosystems” are complex socio-technical systems which can be represented as a multilayer dynamic network. Many of these libraries and software packages are open-source and developed in the open on sites such as , so there is a large amount of data available about these networks. Studying these networks could be of interest to anyone choosing or designing a programming language. In this work, we use tensor factorisation to explore the dynamics of communities of software, and then compare these dynamics between languages on a dataset of approximately 1 million software projects. We hope to be able to inform the debate on software dependencies that has been recently re-ignited by the malicious takeover of the npm package and other incidents through giving a clearer picture of the structure of software dependency networks, and by exploring how the choices of language designers—for example, in the size of standard libraries, or the standards to which packages are held before admission to a language ecosystem is granted—may have shaped their language ecosystems. We establish that adjusted mutual information is a valid metric by which to assess the number of communities in a tensor decomposition and find that there are striking differences between the communities found across different software ecosystems and that communities do experience large and interpretable changes in activity over time. The differences between the elm and R software ecosystems, which see some communities decline over time, and the more conventional software ecosystems of Python, Java and JavaScript, which do not see many declining communities, are particularly marked.

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Epidemic spreading on evolving networks

International Journal of Modern Physics B ◽

10.1142/s0217979219502667 ◽

2019 ◽

Vol 33 (23) ◽

pp. 1950266 ◽

Cited By ~ 2

Author(s):

Jin-Xuan Yang

Keyword(s):

Preferential Attachment ◽

Small World ◽

Network Evolution ◽

Epidemic Spreading ◽

Epidemic Threshold ◽

Scale Free ◽

Evolving Networks ◽

Power Law Exponent ◽

Free Network ◽

Over Time

Network structure will evolve over time, which will lead to changes in the spread of the epidemic. In this work, a network evolution model based on the principle of preferential attachment is proposed. The network will evolve into a scale-free network with a power-law exponent between 2 and 3 by our model, where the exponent is determined by the evolution parameters. We analyze the epidemic spreading process as the network evolves from a small-world one to a scale-free one, including the changes in epidemic threshold over time. The condition of epidemic threshold to increase is given with the evolution processes. The simulated results of real-world networks and synthetic networks show that as the network evolves at a low evolution rate, it is more conducive to preventing epidemic spreading.

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