Source code for networkx.algorithms.time_dependent

"""Time dependent algorithms."""

import networkx as nx
from networkx.utils import not_implemented_for

__all__ = ["cd_index"]

[docs] @not_implemented_for("undirected") @not_implemented_for("multigraph") @nx._dispatchable(node_attrs={"time": None, "weight": 1}) def cd_index(G, node, time_delta, *, time="time", weight=None): r"""Compute the CD index for `node` within the graph `G`. Calculates the CD index for the given node of the graph, considering only its predecessors who have the `time` attribute smaller than or equal to the `time` attribute of the `node` plus `time_delta`. Parameters ---------- G : graph A directed networkx graph whose nodes have `time` attributes and optionally `weight` attributes (if a weight is not given, it is considered 1). node : node The node for which the CD index is calculated. time_delta : numeric or timedelta Amount of time after the `time` attribute of the `node`. The value of `time_delta` must support comparison with the `time` node attribute. For example, if the `time` attribute of the nodes are `datetime.datetime` objects, then `time_delta` should be a `datetime.timedelta` object. time : string (Optional, default is "time") The name of the node attribute that will be used for the calculations. weight : string (Optional, default is None) The name of the node attribute used as weight. Returns ------- float The CD index calculated for the node `node` within the graph `G`. Raises ------ NetworkXError If not all nodes have a `time` attribute or `time_delta` and `time` attribute types are not compatible or `n` equals 0. NetworkXNotImplemented If `G` is a non-directed graph or a multigraph. Examples -------- >>> from datetime import datetime, timedelta >>> G = nx.DiGraph() >>> nodes = { ... 1: {"time": datetime(2015, 1, 1)}, ... 2: {"time": datetime(2012, 1, 1), "weight": 4}, ... 3: {"time": datetime(2010, 1, 1)}, ... 4: {"time": datetime(2008, 1, 1)}, ... 5: {"time": datetime(2014, 1, 1)}, ... } >>> G.add_nodes_from([(n, nodes[n]) for n in nodes]) >>> edges = [(1, 3), (1, 4), (2, 3), (3, 4), (3, 5)] >>> G.add_edges_from(edges) >>> delta = timedelta(days=5 * 365) >>> nx.cd_index(G, 3, time_delta=delta, time="time") 0.5 >>> nx.cd_index(G, 3, time_delta=delta, time="time", weight="weight") 0.12 Integers can also be used for the time values: >>> node_times = {1: 2015, 2: 2012, 3: 2010, 4: 2008, 5: 2014} >>> nx.set_node_attributes(G, node_times, "new_time") >>> nx.cd_index(G, 3, time_delta=4, time="new_time") 0.5 >>> nx.cd_index(G, 3, time_delta=4, time="new_time", weight="weight") 0.12 Notes ----- This method implements the algorithm for calculating the CD index, as described in the paper by Funk and Owen-Smith [1]_. The CD index is used in order to check how consolidating or destabilizing a patent is, hence the nodes of the graph represent patents and the edges show the citations between these patents. The mathematical model is given below: .. math:: CD_{t}=\frac{1}{n_{t}}\sum_{i=1}^{n}\frac{-2f_{it}b_{it}+f_{it}}{w_{it}}, where `f_{it}` equals 1 if `i` cites the focal patent else 0, `b_{it}` equals 1 if `i` cites any of the focal patents successors else 0, `n_{t}` is the number of forward citations in `i` and `w_{it}` is a matrix of weight for patent `i` at time `t`. The `datetime.timedelta` package can lead to off-by-one issues when converting from years to days. In the example above `timedelta(days=5 * 365)` looks like 5 years, but it isn't because of leap year days. So it gives the same result as `timedelta(days=4 * 365)`. But using `timedelta(days=5 * 365 + 1)` gives a 5 year delta **for this choice of years** but may not if the 5 year gap has more than 1 leap year. To avoid these issues, use integers to represent years, or be very careful when you convert units of time. References ---------- .. [1] Funk, Russell J., and Jason Owen-Smith. "A dynamic network measure of technological change." Management science 63, no. 3 (2017): 791-817. """ if not all(time in G.nodes[n] for n in G): raise nx.NetworkXError("Not all nodes have a 'time' attribute.") try: # get target_date target_date = G.nodes[node][time] + time_delta # keep the predecessors that existed before the target date pred = {i for i in G.pred[node] if G.nodes[i][time] <= target_date} except: raise nx.NetworkXError( "Addition and comparison are not supported between 'time_delta' " "and 'time' types." ) # -1 if any edge between node's predecessors and node's successors, else 1 b = [-1 if any(j in G[i] for j in G[node]) else 1 for i in pred] # n is size of the union of the focal node's predecessors and its successors' predecessors n = len(pred.union(*(G.pred[s].keys() - {node} for s in G[node]))) if n == 0: raise nx.NetworkXError("The cd index cannot be defined.") # calculate cd index if weight is None: return round(sum(bi for bi in b) / n, 2) else: # If a node has the specified weight attribute, its weight is used in the calculation # otherwise, a weight of 1 is assumed for that node weights = [G.nodes[i].get(weight, 1) for i in pred] return round(sum(bi / wt for bi, wt in zip(b, weights)) / n, 2)