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. Nodes with high betweenness often play a broker role in the network. 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.īetweenness 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. This can be interpreted as the node's 'outreach'. 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.Ġut-degree: these are the number of edges starting from a certain node. In-degree: these are the number of edges pointing to a certain node. In a directed network, a distinction is made between in-degree and out-degree: Regarding people, this can be seen as a popularity measure, since having many links implies that that person has many acquaintances. To bring out the structure in our networks more clearly, we use layout algorithms such as ' ForceAtlas2' or ' YifanHu'.ĭegree centrality (or simply 'degree'): this is a basic centrality measure that counts the number of ties a node has. 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. In principle the shape of the network is irrelevant: as long as nodes are connected, they can be placed anywhere. In our networks, we have limited the information to properties pertaining to certain nodes. 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. 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). The nature of the connection can be chosen freely. 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). 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. Networks are ways to map connections between elements. On this page, you can browse and sample our finest selection in the menu, brush up your basic network theory (and pick up some tips on how get the most out of these online visualizations), and if you want to have a go at making networks yourself, we can help you get started as well. With all the information we’ve got in Trismegistos, there’s much more where that came from! Our editor database provided us with plenty of data to create co-authorship networks. Names can also be noodlized: in the Trismegistos People section, each name page visualizes how it is linked to other names by means of genealogical relationships. The same criterion can be used to create a network of toponyms. Since Trismegistos has a lot of texts, and those texts mention a lot of people, a network of people appearing in the same text is easily concocted. But when you think of it, almost everything is related to something else in our world, so if you’re creative, you can probably come up with a lot of things to take a look at from a network perspective. The most straightforward type are social relations of course, interactions between individuals.

What do spaghetti monsters do, you ask? Networks are the perfect tool to study relations.

In technical lingo, these are also known as spaghetti monsters. On some Trismegistos pages, you can discover delightfully stringy contraptions called networks. Perhaps you’ve noticed, perhaps you haven’t.

Lingo networks