phasik.classes.TemporalNetwork.TemporalNetwork

class TemporalNetwork

Base class for temporal networks

Temporal networks are networks with time-varying edges. They consist of nodes and edges, and latter can have time-varying weights.

Variables

• nodes (list of (str or int)) – Sorted list of node names. Node names can be either strings or integers, but they all need to be of the same type.

• times (list of (int or float)) – Sorted list of times for which we have temporal information

• tedges (pandas.DataFrame) – Dataframe containing tedges, also called timestamped data (potentially weighted). Columns are [‘i’, ‘j’, ‘t’, (‘weight’)] and each row represents a tedge.

• snapshots (numpy array) – Array of shape (T, N, N) storing the instantaneous values of the adjacency matrix A_{ij}(t).

__init__()

Methods

N()	Returns the number of nodes
T()	Returns the number of times
__init__()
add_tedges(tedges_to_add)	Adds multiple tedges (optionally weighted)
aggregated_network([time_indices, output])	Returns a time-aggregated network as a networkx.Graph
discard_temporal_info_from_edge(edge[, ...])	Discards temporal information from 'edge' by setting its weight to a constant
discard_temporal_info_from_node(node[, ...])	Discards temporal information from 'node' by setting the weight of its edges to a constant
edge_timeseries([edges])	Returns dict of edge time series.
edges_aggregated()	Returns a list of edges in the aggregated network
from_edge_timeseries(edge_timeseries[, ...])	Creates a TemporalNetwork from a DataFrame of edge timeseries
from_node_timeseries(node_timeseries[, ...])	Creates a temporal network by combining node timeseries into edge timeseries.
from_static_network_and_edge_timeseries(...)	Creates a temporal network by combining a static network with edge timeseries
from_static_network_and_node_timeseries(...)	Creates a temporal network by combining a static network with node timeseries
from_static_network_and_tedges(...[, ...])	Creates a temporal network by combining a static network with tedges
from_tedges(tedges[, normalise])	Creates a TemporalNetwork from a dataframe of tedges
has_node(node)	Returns True if node is in the TemporalNetwork
has_tedge(tedge)	Returns True if tedge is in the TemporalNetwork, regardless of its weight
has_time(time)	Returns True if time is in the TemporalNetwork
is_weighted()	Returns True if tedges are weighted
neighbors()	Returns a dictionary of neighboring nodes in the aggregate network.
network_at_time(time_index[, output])	Returns the temporal network at time 'time' as a networkx.Graph
number_of_edges()	Returns the number of edges in the aggregated network
shape()	Returns the shape (N,T) of the TemporalNetwork
tedges_of_edge(edge[, return_mask, reverse])	Returns a filtered DataFrame containing only the tedges of edge 'edge'.
tedges_of_node(node[, return_mask, reverse])	Returns a filtered DataFrame containing only the tedges of node 'node'.
to_partially_temporal()	Returns a copy of the temporal network as a PartiallyTemporalNetwork

Attributes

nodes	Returns a list of nodes in the TemporalNetwork
snapshots	Returns a numpy array of snapshots in the TemporalNetwork
tedges	Returns a DataFrame of tedges the TemporalNetwork
times	Returns a list of times in the TemporalNetwork