In network science, **efficiency** is a measure of how effectively information, resources, or energy can be transmitted through a network. It is an important concept for understanding the performance and dynamical processes that occur in various types of networks, such as social networks, biological networks, communication networks, and transportation systems. There are two primary types of efficiency often discussed in the context of network science: 1. **Global Efficiency**: This measures how easily information can be transmitted across the entire network.

The evolution of a random network generally refers to how the structure and properties of a random network change over time or under certain conditions. Random networks are mathematical models used to describe networks where connections (or edges) between nodes (or vertices) are generated randomly according to specific probabilistic rules. Evolution can be studied in various contexts, including: 1. **Growth Models**: Many random networks are created using growth models that involve the addition of nodes over time.

First passage percolation (FPP) is a stochastic process that is used to model the spread of fluid or information through random media. It is often studied in the context of mathematical probability, statistical physics, and networks.

The Fitness Model in network theory is a framework used to understand and describe the formation and evolution of complex networks, particularly focusing on the distribution of connectivity among nodes. This model is typically used in the context of biological, social, and technological networks, where the connections between nodes (which can represent anything from genes to individuals to websites) are not uniform but rather influenced by varying degrees of "fitness" or attractiveness.

Fractal dimension is a concept that extends the idea of dimension beyond the traditional integer dimensions (like 1D, 2D, 3D) to describe complex, self-similar structures that may not fit neatly into these categories. In the context of networks, the fractal dimension is used to quantify the complexity of the network's structure and how it scales as the size of the network increases.

In network science, a "hub" refers to a node (or vertex) within a network that has a significantly higher degree of connectivity compared to other nodes. In simpler terms, a hub is a node that is connected to a large number of other nodes, making it a central point of interaction within the network. Hubs play a crucial role in various types of networks, including social networks, transportation networks, and biological networks.

A hyperbolic geometric graph is a type of graph that is embedded within a hyperbolic space, which is a non-Euclidean geometric space characterized by a constant negative curvature. Hyperbolic geometry has unique properties that differentiate it from Euclidean geometry, particularly in terms of parallel lines, triangle sums, and the relationship between distances and angles. In hyperbolic geometric graphs, the vertices can represent points in hyperbolic space, and the edges can represent relationships or connections between these points.

An incomplete information network game is a type of strategic interaction model where players engage in decision-making on a network but possess limited knowledge about certain aspects of the game. Specifically, the information can be incomplete regarding the preferences, types, strategies, or payoffs of the other players involved in the game. Key components of an incomplete information network game include: 1. **Network Structure**: The players are situated within a network, which represents the connections or relationships among them.

The Louvain method is a popular algorithm used for community detection in large networks. It is named after the university town of Louvain in Belgium, where the method was developed. The primary goal of the Louvain method is to identify clusters or communities within a graph, where nodes are more densely connected among themselves than with nodes outside the community. The algorithm operates on the principle of optimizing modularity, which is a measure of the quality of the partitioning of the network into communities.

Network science is an interdisciplinary field that studies complex systems represented as networks. It focuses on understanding the relationships and interactions among various entities, which can be anything from social connections among individuals, to biological interactions in ecosystems, to connectivity in communication networks or transportation systems. Key concepts in network science include: 1. **Nodes and Edges**: The basic building blocks of a network are nodes (the individual entities) and edges (the connections or relationships between them).

Low-degree saturation is a term often used in the context of polynomial interpolation, computational algebra, and related fields that deal with functions or structures defined over finite fields or rings. It generally refers to properties of polynomials that involve the number of variables and the degree of polynomials. In general, saturation in mathematical contexts involves the idea of filling up or reaching a maximum capacity.

The Mediation-driven Attachment Model (MAM) is a framework in psychology and psychotherapy that focuses on understanding how attachment styles—patterns of how individuals relate to others based on their early experiences with caregivers—can influence relationships and emotional well-being. The model often examines the role of mediating variables that influence the relationship between attachment styles and various psychological outcomes.

A multidimensional network is a type of network that allows for multiple types of relationships or interactions between entities (or nodes). Unlike traditional networks, which often represent a single type of relationship (for example, social connections in a social network or collaborations in a co-authorship network), multidimensional networks incorporate various types of connections within the same structure. ### Key Characteristics: 1. **Multiple Layers:** Each type of relationship can be thought of as a separate layer in the network.

A narrative network refers to a structured system or framework that organizes and connects various narrative elements, such as characters, events, themes, and plots. In the context of storytelling, a narrative network can help map out the relationships between different narratives and how they interact with one another.

Network Science CTA typically refers to "Network Science Community-Trusted Authority" or similar concepts associated with network science. However, it could also refer to specific initiatives, organizations, or frameworks that are focused on the study and analysis of complex networks. Network science is an interdisciplinary field that studies networks of various kinds, such as social networks, biological networks, computer networks, and more. It combines elements of mathematics, physics, and computer science to understand the structure and behavior of networks.

Network homophily is a concept from sociology and network theory that refers to the tendency of individuals to associate and bond with others who are similar to themselves in various attributes, such as age, gender, race, education, socioeconomic status, or values. The principle of homophily suggests that "birds of a feather flock together," meaning that people are more likely to form connections with those who share similar characteristics or beliefs.

Tribe is a social networking platform designed for community building and engagement. It allows individuals and organizations to create branded online spaces, known as "tribes," where members can connect, share content, discuss topics, and collaborate on projects. The platform is focused on fostering meaningful interactions and relationships within these communities. Key features of Tribe might include: - Customizable community spaces with branding options. - Discussion forums and threads for topic-centric conversations.

Beckstrom's Law is a principle used primarily in the field of network theory and economics, particularly in the context of information networks. Proposed by economist and information scientist Ronald Beckstrom in 2008, the law attempts to articulate the value of a network in terms of the transactions that occur within it.

Network theory in risk assessment refers to the application of network analysis and modeling techniques to understand and evaluate the risks associated with complex systems. This approach is particularly useful in environments where elements are interconnected, and the interactions between them can create cascading effects or emergent risks. Here are some key aspects of network theory in risk assessment: 1. **Understanding Interdependencies**: Network theory allows analysts to visualize and model how different components of a system (e.g.

In labor economics, "networks" refer to the social connections and relationships among individuals that can influence various aspects of labor market outcomes, including job search, hiring processes, and career advancement. These networks can take many forms, including personal connections, professional associations, family ties, or community groups, and they play a significant role in how information about job opportunities is disseminated and how individuals access those opportunities.