Number of nodes.
Node weights.
Index of the first neighbor of each node in the neighbors array.
The neighbors of node i are given by
neighbors[firstNeighborIndices[i]], ..., neighbors[firstNeighborIndices[i + 1] - 1].
Neighbors of each node.
Edge weights.
Total edge weight of self links.
Number of edges.
Each edge is counted twice, once in each direction.
Constructs a network based on a list of edges or neighbors.
Network constructor parameters
Returns the number of nodes.
Number of nodes
Returns the total node weight.
Total node weight
Returns the weight of each node.
Weight of each node
Returns the weight of a node.
Node
Weight
Returns the number of edges.
Number of edges
Returns the number of neighbors per node.
Number of neighbors per node
Returns the number of neighbors of a node.
Node
Number of neighbors
Returns the list of edges.
List of edges
Returns a list of neighbors per node.
List of neighbors per node
Returns the list of neighbors of a node.
Node
List of neighbors
Returns the total edge weight per node. The total edge weight of a node equals the sum of the weights of the edges between the node and its neighbors.
Total edge weight per node
Returns the total edge weight.
Each edge is considered only once, even though an edge runs in two directions. This means that the sum of the edge weights returned by getEdgeWeights equals twice the total edge weight returned by getTotalEdgeWeight.
Edge weights of self links are not included.
Node
Total edge weight
Returns a list of edge weights per node. These are the weights of the edges between a node and its neighbors.
List of edge weights per node
Returns the list of edge weights of a node. These are the weights of the edges between the node and its neighbors.
Node
List of edge weights
Returns the total edge weight of self links.
Total edge weight of self links
Creates a copy of the network, but without node weights.
Each node is assigned a weight of 1.
Network without node weights
Creates a copy of the network, but without edge weights.
Each edge is assigned a weight of 1.
Network without edge weights
Creates a copy of the network, but without node and edge weights.
Each node is assigned a weight of 1, and each edge is assigned a weight of 1.
Network without node and edge weights
Creates a copy of the network in which the edge weights have been normalized using the association strength.
The normalized weight a'[i][j] of the edge between nodes i and j is
given by
a'[i][j] = a[i][j] / (n[i] * n[j] / (2 * m)),
where a[i][j] is the non-normalized weight of the edge between nodes i
and j, n[i] is the weight of node i, and m is half the total node
weight.
If each node's weight equals the total weight of the edges between the node and its neighbors, the edge weights are normalized by dividing them by the expected edge weights in the random configuration model.
The node weights are set to 1.
Normalized network
Creates a copy of the network in which the edge weights have been normalized using fractionalization.
The normalized weight a'[i][j] of the edge between nodes i and j is
given by
a'[i][j] = a[i][j] * (n / n[i] + n / n[j]) / 2,
where a[i][j] is the non-normalized weight of the edge between nodes i
and j, n[i] is the weight of node i, and n is the number of nodes.
The node weights are set to 1.
Normalized network
Creates a copy of the network that has been pruned in order to have a specified maximum number of edges.
Only the edges with the highest weights are retained in the pruned network. In case of ties, the edges to be retained are selected randomly.
Maximum number of edges
Random number generator
Pruned network
Creates an induced subnetwork for specified nodes.
Nodes
Subnetwork
Creates an induced subnetwork for specified nodes.
Indicates the nodes to be included in the subnetwork.
Subnetwork
Creates an induced subnetwork for a specified cluster in a clustering.
If subnetworks need to be created for all clusters in a clustering, it is more efficient to use createSubnetworks.
Clustering
Cluster
Subnetwork
Creates induced subnetworks for the clusters in a clustering.
Clustering
Subnetworks
Creates an induced subnetwork of the largest connected component.
Subnetwork
Creates a reduced (or aggregate) network based on a clustering.
Each node in the reduced network corresponds to a cluster of nodes in the original network. The weight of a node in the reduced network equals the sum of the weights of the nodes in the corresponding cluster in the original network. The weight of an edge between two nodes in the reduced network equals the sum of the weights of the edges between the nodes in the two corresponding clusters in the original network.
Clustering
Reduced network
Identifies the connected components of the network.
Connected components
Checks the integrity of the network.
It is checked whether:
An exception is thrown if the integrity of the network is violated.
Generated using TypeDoc
Network.
Weighted nodes and weighted edges are supported. Directed edges are not supported.
Network objects are immutable.
The adjacency matrix of the network is stored in a sparse compressed format.