Networks are ways to map connections between elements. Many networks concern relationships between people, but anything can be the subject of a network, as long as there's a relation between the elements. Each person, or settlement, or name, etc., is represented by a node (the dots), and each connection is shown as an edge (or link or tie: the lines between the dots).

The nature of the connection can be chosen freely. A network can be undirected, i.e. all ties are reciprocal, or directed if there is a certain "information flow" from one node to the other (this type of connection is indicated by an arrow at the end of the edges).

There are many network measures to describe the features of a network, either as a whole, as well as those of individual nodes and ties: betweenness centrality, eigenvector, density, modularity, ... In our networks, we have limited the information to properties pertaining to certain nodes.

In principle the shape of the network is irrelevant: as long as nodes are connected, they can be placed anywhere. But as humans have difficulty seeing the jumble of edges between the thousands of nodes in the large networks we create, programs like Gephi can adapt the visual representation of the network according to specific layout algoritms. To bring out the structure in our networks more clearly, we use layout algorithms such as 'ForceAtlas2' or 'YifanHu'.

Degree centrality (or simply 'degree'): this is a basic centrality measure that counts the number of ties a node has. Regarding people, this can be seen as a popularity measure, since having many links implies that that person has many acquaintances. In a directed network, a distinction is made between in-degree and out-degree:

In-degree: these are the number of edges pointing to a certain node. In a directed network, the in-degree is used to measure popularity, since it is an indication of how many sources refer to that particular node.
0ut-degree: these are the number of edges starting from a certain node. This can be interpreted as the node's 'outreach'.
Weighted degree: a connection between two entities can be registered more than once (see above), and this edge weight can be taken into account when calculating degree centralities.

Betweenness centrality: this centrality measure does not count the number of direct links a node has, but rather how often it lies on the shortest path between two other nodes. Nodes with high betweenness often play a broker role in the network. If node A and node C are both connected to B, but not directly to each other, B is crucial to get information from A to C and vice versa.

Geodesic distance and eccentricity: the geodesic distance is the number of steps it takes to get from one node to another, using the shortest path (as in the famous 'six degrees of separation'). A geodesic distance of 1 means that two nodes are directly linked. A geodesic distance of 5 means that node A has to go through minimum 4 other nodes to get to node B. Eccentricity then measures how far a node is from the furthest other connected node, i.e. its largest geodesic distance.

Closeness centrality: again, this centrality measure does not focus on a node's direct links, but rather at how connected it is to the entire network. Closeness is based on the inverse of the distance of a node to all other connected nodes, so here the bottom line is: the lower the result, the better. Node A, despite the fact that it is only connected to node B, can still have a high closeness centrality if node B has many connections to other (central) nodes in the network.

Modularity class: community detection is another network analysis feature. We use the Louvain algorithm in Gephi to detect sub-units in large networks, i.e. highly interconnected groups of nodes with only sparse connections to other groups in the network. By looking at the links between the nodes, the computer assigns them to 'classes' or 'communities', which are indicated by a random number. We can then look for meaningful characteristics that create such a structure. In the geographical networks (network 7 & 8), for example, the communities largely coincide with Egypt's administrative district.