scispace - formally typeset
Search or ask a question

Calculate the density of directed and undirected graph? 


Best insight from top research papers

The density of a subgraph in an undirected graph is defined as the average degree of the subgraph. For directed graphs, the density is defined as the ratio of the number of edges going from one subset of vertices to another subset, divided by the square root of the product of the sizes of the subsets. Finding subgraphs of maximum density is a well-studied problem, and there are polynomial time algorithms available when there are no size constraints. However, when a size constraint is specified, finding a maximum density subgraph becomes NP-hard. There are approximation algorithms available that can find subgraphs within a factor of 2 of the optimum density.

Answers from top 5 papers

More filters
Papers (5)Insight
The paper does not provide information about calculating the density of directed and undirected graphs.
Open accessBook ChapterDOI
Samir Khuller, Barna Saha 
06 Jul 2009
270 Citations
The density of a subgraph in an undirected graph is calculated as the ratio of the number of edges in the subgraph to the number of vertices. In a directed graph, the density is calculated as the ratio of the number of edges going from one subset of vertices to another subset, to the square root of the product of the sizes of the subsets.
The density of a directed graph is defined as the maximum density of any subset of vertices, while the density of an undirected graph is defined as the maximum density of any subset of vertices.
The paper does not provide information about calculating the density of directed and undirected graphs.
Patent
Jin Xiaoliang, Yuan Chunyu 
31 May 2017
The graph density calculation method described in the paper can be used to calculate the density of both directed and undirected graphs.

Related Questions

What is density of graph in graph theory or network science?4 answersThe density of a graph in graph theory or network science refers to the measure of how closely packed the edges are in a graph. It is a topological descriptor that provides information about the distribution of edges in a network. The density of a graph can be determined by calculating the ratio of the number of edges to the maximum possible number of edges in a graph. A higher density indicates a higher level of connectivity in the network, while a lower density indicates a sparser network. The concept of density is important in understanding the structure and connectivity of graphs in various domains such as social networks and the internet.
What is the formula for wind power density?5 answersThe formula for wind power density is not explicitly mentioned in the abstracts provided. However, the abstracts discuss various techniques and models used to evaluate wind power density, such as the Rayleigh distribution function, power law exponent, and Weibull two-parameter density function. These techniques and models are used to estimate the wind power density at a specific site based on factors such as wind speed, frequency distribution, and scale parameters. The accuracy of these techniques is also compared to the standard formula for evaluating wind power density. While the specific formula for wind power density is not mentioned, these techniques and models provide valuable insights into assessing wind power density for different locations and applications.
What is relatedness density?5 answersRelatedness density refers to the degree of correlation or association between individuals or entities in a given system or context. It measures the extent to which individuals or entities are related or connected to each other based on certain criteria or factors. The concept of relatedness density has been explored in various fields, including biology, physics, and information science. In biology, it has been studied in the context of kin selection and the influence of genetic relatedness on growth and survival rates in fish populations. In physics, relatedness density has been investigated in the context of correlated density in physical systems, such as dense interacting gases and superconductors. In information science, it has been used to measure the semantic relatedness between scientific concepts and entities based on the density of connections in an ontology graph.
How to calculate Density in clustering in social network?5 answersDensity in clustering in social networks can be calculated using node-wise measures that quantify the role and position of actors. These measures are used to derive different community configurations, allowing for the identification of a hierarchical structure of clusters. The lack of a probabilistic notion of density in networks is turned into a strength of the proposed method, as it allows for the disentanglement of different involvements of individuals in social aggregations. Additionally, in the context of spatial clustering in geo-social networks, density-based clustering can be extended to consider spatio-temporal information and social relationships between users. This extension takes into account the people who visit the clustered places and evaluates the quality of the discovered clusters using quantitative measures such as social entropy and community score.
What is density in science?5 answersDensity in science refers to the relationship between an object's mass and volume. It is a fundamental concept that is important in various fields such as physics, chemistry, and materials science. Understanding density requires knowledge of foundational measurement concepts and proportional reasoning. Density is used to characterize and differentiate materials, including cells and organisms, and to study their chemical and physical changes over time. In the context of urban planning and architecture, density can also refer to the density of buildings, the density of encounters (randomness and closeness), and the density of uses (differentiation of activities and functions). Density can be studied and analyzed using various techniques, such as magnetic levitation, which has applications in chemistry, materials science, and biochemistry. Density can also be used as a parameter to indicate the diversity of concepts in scientific journals.
What is the theory of density?5 answersThe theory of density is a fundamental concept in various fields of study. In the context of photography, the theory of density refers to the measurement of optical density in a developed emulsion layer, which is important for understanding the spectral and specular properties of light attenuation. In the field of density functional theory, the theory of density focuses on using the electron density as the fundamental variable to explore the ground-state properties of a system of interacting electrons. In the realm of general relativity, the theory of density relates the distribution of matter or energy fields to the geometry of space, with the curvature of space affecting the integration of properties like particle number, mass, or energy density. In geography, the theory of density is used to analyze urban spaces and understand the linkages between human activities, population, and network organization. In approximation theory, the theory of density investigates the possibility of approximating functions by examining whether the given family of functions is dense in the set of functions to be approximated.

See what other people are reading

Which distances are used for clusterization gene graphs?
5 answers
The distances used for clustering gene graphs include various methods such as distance functions and minimum distance calculations. In the study by Favre et al., gene interaction graphs were decomposed using clique minimal separators, where distances between genes were estimated by a distance function to construct gene interaction graphs based on chosen thresholds. Chang et al. developed algorithms for calculating minimum distances between positions on a sequence graph and clustering seeds on a graph efficiently, which is crucial for mapping algorithms. These approaches highlight the importance of accurate distance calculations in clustering gene graphs, enabling a better understanding of gene interactions and functional relationships within biological systems.
How does the apple design thinking method differ from other design thinking methods?
5 answers
The Apple design thinking method, as revealed in interviews with iPhone users, focuses on constituting consumers into Apple subjects through what is termed "the Apple Way". In contrast, the apple-free graph concept in mathematics defines an apple as a specific structure within a graph, leading to efficient problem-solving strategies like the Maximum Weight Independent Set problem. Furthermore, in machine vision technology, an apple defect detection method based on algorithms like Fuzzy C-means and Nonlinear Programming Genetic Algorithm is employed, achieving a high detection accuracy of 98%. These diverse applications of the term "apple" across different fields showcase the varied approaches and methodologies associated with the concept, highlighting the unique perspectives and problem-solving techniques each domain employs.
How to construct static and kinetic graph?
5 answers
To construct static and kinetic graphs, different approaches are outlined in the provided research contexts. For static graphs, one can utilize predefined criteria like degree or distance based on local information. On the other hand, for kinetic graphs, a dynamic approach is needed to capture the continuously changing nature of structures, where edges are represented by time-varying weights. Additionally, in the context of stock prediction, a static-dynamic graph neural network (SDGNN) is proposed to learn both static and dynamic graphs, uncovering potential relations between stocks and enabling interactions between the two types of graphs for improved performance. These approaches highlight the importance of considering both static and kinetic aspects when constructing graphs for various applications.
What are the types of graph?
5 answers
Various types of graphs are discussed in the literature. These include fundamental types like complete, cycle, star, wheel, linear, and grid graphs. Additionally, there are specialized graphs such as commuting graphs, non-commuting graphs, conjugate graphs, and conjugacy class graphs for certain groups. Moreover, the concept of graphs extends to more complex data structures beyond trees, encompassing structures like difference lists, doubly-linked lists, skip lists, and leaf-linked trees, which are handled in a new functional language called $\lambda_{GT}$. Furthermore, in the realm of deep learning, there is a focus on Graph Neural Networks, Message-passing Neural Networks, Relational Networks, and Graph Networks, which allow for the projection of graph vertices into a hyperdimensional real space for iterative refinement in learning tasks.
Domination number of prism over cycle
5 answers
The domination number of the prism over a cycle graph involves studying dominating sets in the prism graph formed by joining two disjoint copies of the cycle graph. Various papers explore different aspects related to domination numbers in graphs. For instance, one paper investigates strong Roman domination numbers in cycle-related graphs, while another paper delves into the relationship between domination and p-domination numbers in graphs and their prism graphs for specific values of p. Additionally, research has been conducted on graphs where the domination number of the prism is related to the domination number of the original graph under certain permutations of the vertex set. These studies collectively contribute to understanding the domination properties of prism graphs over cycles.
Did Martin Grohe mention twin-width?
5 answers
Martin Grohe did not directly mention twin-width in the provided contexts. However, the concept of twin-width, a graph parameter with applications in algorithmics, combinatorics, and finite model theory, has been extensively discussed by various authors such as Hugo Jacob, Marcin Pilipczuk, Édouard Bonnet, Colin Geniet, Romain Tessera, Stéphan Thomassé. The research on twin-width includes characterizations for hereditary classes of totally ordered graphs, its preservation by quasi-isometry for graphs of bounded degree, and its implications in defining group invariants based on exclusion patterns in the self-action by product of group elements. While Martin Grohe's direct mention of twin-width is not found, the concept has been explored in depth by other researchers in the field.
Why are clubs time consuming?
5 answers
Clubs can be time-consuming due to various factors. The scheduling of operations at a tennis school within a general tennis club is a manual and time-consuming task, taking 2-4 weeks for the responsible person. Time management is crucial in sports management, with a focus on efficient use of resources and scheduling according to a programmed curriculum. Additionally, the placement of peripherally inserted central venous catheters (PICCs) in pediatric intensive care units (PICUs) involves varying providers, leading to delays and challenges in acquiring central vascular access. Moreover, the computation of maximum s-clubs in graphs is an NP-hard problem, requiring significant attention and time for solving. These factors collectively contribute to the time-consuming nature of clubs in different contexts.
How many edges does a complete graph contain?
5 answers
A complete graph with v vertices contains v(v-1)/2 edges, as each vertex is connected to every other vertex in the graph. In the context of graph designs, the papers discuss partitions of complete graphs into subgraphs isomorphic to a given graph G. Topological complete graphs are defined in a way that ensures every pair of vertices is connected by an edge, resulting in a maximal number of edges for a given number of vertices. Turan's Theorem provides insights into the edge density required to guarantee the existence of a complete graph within a given graph G, based on the number of vertices and edges in G. Therefore, a complete graph contains a specific number of edges determined by its vertices, with implications for various graph theoretical concepts.
What is the definition of transferability according to Lincoln?
4 answers
Transferability, as defined in various contexts, refers to the ability to transfer methods, models, information, or systems from one domain or environment to another while maintaining effectiveness and relevance. In the realm of interactive systems design, transferability involves selecting and applying methods that suit the specific interactive system being developed, considering new contexts of use and ensuring universal access. In the field of molecular components and chemical reactions, approximate transferability is crucial for classifying reactions and molecular families based on functional groups and reaction types. When it comes to deep geological disposal of radioactive waste, transferability involves transferring external information to safety cases for specific projects, focusing on parameters, investigation techniques, and conceptual models. In the domain of spectral graph convolutional neural networks, transferability refers to the network's capacity to generalize across different graphs representing the same phenomenon.
How to plot graph static and kinetic?
5 answers
To plot a graph illustrating static and kinetic aspects, one can draw inspiration from various fields. For instance, in the realm of physics, the distinction between static and kinetic friction can be visually represented through graphical demonstrations, aiding in understanding the transition between the two states. In the domain of Mobile Ad Hoc Networks (MANET), the concept of Kinetic Graphs offers a method to capture dynamic changes in network structures over time, showcasing the evolution of criteria such as degree or distance through time-varying weights on edges. Moreover, in the context of runtime systems and parallel execution, the use of Kinetic Dependence Graphs (KDG) allows for the visualization of complex task dependencies that dynamically evolve based on runtime values, enabling programmers to exploit parallelism effectively. By integrating insights from these diverse fields, one can create informative graphs that depict the interplay between static and kinetic elements.
What does a high patch cohesion index mean?
4 answers
A high patch cohesion index signifies a strong level of connectivity and continuity within ecological networks or urban green infrastructures. In the context of ecological networks, a high cohesion metric indicates the presence of well-connected habitat patches with dense tree cover and high ecosystem standards, facilitating biodiversity conservation. Similarly, in software design, high cohesion in object-oriented programming signifies a strong binding of class elements, leading to classes that are less prone to faults and easier to develop and maintain. Moreover, in the context of social cohesion within residential complexes, a high residential social cohesion index reflects strong social relations among residents, as identified through self-reported questionnaires and expert surveys in high-rise group housing complexes.