Network Graph

When thinking about the representation of geodata on maps, a connection to network graphs might not immediately come to mind. In this course, however, thinking outside the box is emphasized. From a graph point of view, TSP is called a Hamiltonian cycleThe Hamiltonian cycle is named after William Rowan Hamilton, an Irish mathematician who made significant contributions to graph theory., which is a closed loop that visits each vertex (store) exactly once. Instead of referring to them as store locations, we can also call them nodes. Every node will be visited once, so every node has exactly one predecessor and one successor.

Network graph

Network graphs (sometimes called knowledge graphs) represent a collection of interconnected entities organized into contexts via links and semantic metadata. They provide a framework for data integration and analysis. The ability to do this can provide more context to the metrics recorded by a network system. By using network graphs, we can improve our understanding of the data.

Network graphs consist of three main components: nodes, edges, and weights.

Nodes

In network graphs, nodes represent any tangible entity, such as locations. Node is a more sophisticated term for bubble.

Edges

If the nodes are related to each other, this is shown as an edge. The edge defines the specific relationship and reason for the shared connection between these two nodes. Both the node and the edge contain attributes that help describe the relationship of the network.

Weights

An edge is given a weight (thickness or length of the edge’s line) to define the strength of the relationship between any two nodes. An edge can also display a direction (arrow), which represents the flow or relationship between the nodes it connects. Four types of weights are possible when defining relationships: edges can be directed, undirected, weighted, or unweighted.