Network models are very important to understand organisational principles or to understand if an observed property of a network has occurred by chance or if there is something "special" about it. Over the years many different network models have been developed. We will talk briefly about the most important ones, where the importance is to be understood in their popularity in brain research or because they are "extremes" which are necessary to understand, for example in the context of network topology.
Ordered networks are commonly referred to as lattice networks. In these network models nodes are connected to their nearest neighbours, resulting in a large clustering coefficient. It represents one of the extreme ends of the network spectrum, which is uncommon in the real world, as information transport along the edges in the network takes relatively long. However, lattice networks are very resilient to attacks. If a particular node was knocked out, the overall network structure most likely would not suffer.
Lattice networks are sometimes referred to as geometric networks, as they may take the geometric positions of the nodes into account. This may be important, when networks are not only investigated in two dimensions, but in three, as they can be found for example in the brain. Visually representing large networks as above is challenging in three dimensions. In these cases it is common to represent a network with N nodes by their adjacency matrix - a matrix of size NxN filled with zeros and ones, depending if an edge between two nodes exist (one) or not (zero).
A circular lattice network, like the one above, can be represented by the adjacency matrix on the left (200x200 matrix, existing edges coloured in white). However, for example in brain network and when taking the geodesic distance (the distance along the surface) into account, the adjacency matrix is usually not that simple (matrix on the right).
As mentioned before, there are many ways to generate random networks. The first binary random graph model was proposed by Erdős-Rényi (ER). It is based on the assumption that the existence of an edge is independent of any node property, resulting in a constant probability of an edge between any pairs of nodes existing.
The ER graph is an easy way to generate a random network. However, unlike real networks, ER random graphs usually do not produce any hubs (nodes which have many more edges than the average in the graph). One interesting property, however, is the emergence of the giant component. Once the average nodal degree reaches one, a large amount of nodes become connected. In particular when considering information or disease transfer, the giant component allows for an extensive spread.
Other models include for example the preferential attachment model introduced by Barabási and colleagues. It is a dynamic network model, which means that it incorporates a time component. At each time step a new node is introduced into the network, which connects with one or more of the existing nodes depending on how long the nodes have been present in the network (the older the node, the higher the probability that a new node forms a connection with it). A good example of this kind network can be found when analysing the citations of scientific publications.
In order to assess if a given property is "special" in an observed network, it is important to compare it to a random realisation which shares some of its properties. These random networks can be seen as a baseline to which the observed network can be compared. Random realisations can be achieved for example by using the ER model as described before, and matching the number of nodes and number of edges. Another set of random graphs are the so-called exponential random graph models (ERGM). The user can specify any given parameter, such as values of a given network measure, and the ERGM then draw samples randomly from the set of all graphs that share this property. This approach, however, is computationally very expensive.
A very common method used to create surrogate networks in brain network analyses is based on the principle of pairwise switching. It randomises the edges of an observed graph, while keeping the degree of each node constant.
The principle is as follows: Two nodes in the graph are picked randomly (for example r and s). From the neighbourhood of each of these nodes (the nodes that are directly connected to them) a neighbour is selected randomly (for example t and u). In the next step the neighbours are exchanged. In the example above, this means that node s is connected to node t and node r to node u.