# Identify the connectivity of each relationship

### Identifying Connectivity Relationships

Identify the connectivity for each relationship. d. Identify the mandatory/optional dependencies for the relationships. e. Resolve all M:N relationships. In mathematics and computer science, connectivity is one of the basic concepts of graph theory: An undirected graph is connected when there is a path between every pair of vertices. . More generally, it is easy to determine computationally whether a graph is connected (for example, by using a disjoint- set data structure). Connectivity relationships are ways in which resources defined in RODM can be connected to each other. These relationships can be physical, logical, or peer.

An undirected graph is connected when there is a path between every pair of vertices.

In a connected graph, there are no unreachable vertices. A graph that is not connected is disconnected. An undirected graph G is said to be disconnected if there exist two nodes in G such that no path in G has those nodes as endpoints.

A graph with just one vertex is connected.

## Identifying Connectivity Relationships

An edgeless graph with two or more vertices is disconnected. Definitions of components, cuts and connectivity[ edit ] In an undirected graph G, two vertices u and v are called connected if G contains a path from u to v. Otherwise, they are called disconnected.

**Lecture 4 Pixel Relationships**

If the two vertices are additionally connected by a path of length 1, i. A graph is said to be connected if every pair of vertices in the graph is connected. A connected component is a maximal connected subgraph of G. Each vertex belongs to exactly one connected component, as does each edge. A directed graph is called weakly connected if replacing all of its directed edges with undirected edges produces a connected undirected graph.

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Both of these explanations are possible, but they are difficult to disentangle using divergence-based approaches. The development of techniques that use both polymorphism and divergence to infer the strength of positive and negative selection hold promise for distinguishing between the relative roles of positive and negative selection in shaping divergence Keightley and Eyre-Walker ; Eyre-Walker and Keightley In this study we investigate the strength of negative and positive selection across gene coexpression networks constructed from natural variation for gene expression level.

A coexpression network is an undirected graph where the nodes correspond to different genes, and genes that exhibit a significant correlation in expression level are connected. Gene connectivity is calculated as the sum of the strengths of correlations between a focal gene and all other genes Langfelder and Horvath We take the second approach, investigating coexpression in a population sample, measured in one environment and tissue type, and constructing coexpression networks that summarize expression variation between individuals.

Connectivity measured in coexpression networks generated using a population sample will likely reflect, at least in part, genetic variation for gene regulation that segregates in the population.

- Connectivity (graph theory)

Large-effect cis-regulatory variants can reduce correlations in expression between the genes they regulate and other genes, so the presence of cis-regulatory variants may influence measurements of gene connectivity. Similarly, expression level may correlate with measures of connectivity. Here, we evaluate the relationship between coexpression network connectivity and both positive and negative selection in the plant Capsella grandiflora.

We find that connectivity is negatively correlated with amino acid divergence and show that this correlation is driven by stronger negative selection on highly connected genes, even when controlling for gene expression level.

However, the relationship between negative selection and connectivity is confounded by the presence of local regulatory variation, which both reduces connectivity as measured using coexpression networks and is associated with lower levels of negative selection. Our results are consistent with two possibilities: Materials and Methods Measuring Expression We used leaf expression data from a population sample of 99 individuals of C.

We grew an individual seed collected from each wild parent in the University of Toronto greenhouses and conducted independent random crosses to generate the seeds used in the studies. Four weeks after transplanting, leaf tissue from all individuals was collected and immediately flashes frozen in liquid nitrogen.

All samples were collected sequentially. We extracted RNA from two or three samples per plant using plant RNA extraction kits Sigma and used a Qubit spectrophotometer to quantify RNA concentration so that the samples from each plant could be pooled such that each pool contained equal amounts of RNA from each sample.

One of the employees runs each division. The Hudson Engineering Group HEG has contacted you to create a conceptual model whose application will meet the expected database requirements for its training program. The HEG administrator gives you the following description of the training group's operating environment: The HEG has twelve instructors and can handle up to thirty trainees per class. HEG offers five "advanced technology" courses, each of which may generate several classes.

If a class has fewer than ten trainees in it, it will be canceled. It is, therefore, possible for a course not to generate any classes during a session.

Each class is taught by one instructor.

### Connectivity (graph theory) - Wikipedia

Each instructor may teach up to two classes or may be assigned to do research. Each trainee may take up to two classes per session. Given this information, do the following: Describe the relationship between instructor and course in terms of connectivity, cardinality, and existence dependence. Given the following business rules, create the appropriate E-R diagram for each of the specified relationships: