Node metrics and functions#

Nodes can be accessed by storing the object returned from a call to add_node(), by directly asking for a specific entity via node(), or by iterating over all entities via nodes. Once you have a node, you can ask it some questions.

Update history#

Nodes have functions for querying their earliest and latest update time (as an epoch or datetime) as well as for accessing their full history (using history() or history_date_time()). In the code below we create a node object for the monkey Felipe and see when their updates occurred.

Python

import pandas as pd
from raphtory import Graph
from datetime import datetime

edges_df = pd.read_csv(
    "../data/OBS_data.txt", sep="\t", header=0, usecols=[0, 1, 2, 3, 4], parse_dates=[0]
)
edges_df["DateTime"] = pd.to_datetime(edges_df["DateTime"])
edges_df.dropna(axis=0, inplace=True)
edges_df["Weight"] = edges_df["Category"].apply(
    lambda c: 1 if (c == "Affiliative") else (-1 if (c == "Agonistic") else 0)
)

g = Graph()
g.load_edges_from_pandas(
    df=edges_df,
    src="Actor",
    dst="Recipient",
    time="DateTime",
    layer_col="Behavior",
    properties=["Weight"],
) 

v = g.node("FELIPE")
print(
    f"{v.name}'s first interaction was at {v.earliest_date_time} and their last interaction was at {v.latest_date_time}\n"
)
history = v.history_date_time()
# We format the returned datetime objects here to make the list more readable
history_formatted = [date.strftime("%Y-%m-%d %H:%M:%S") for date in history]

print(f"{v.name} had interactions at the following times: {history_formatted}\n")

Output

FELIPE's first interaction was at 2019-06-13 09:50:00+00:00 and their last interaction was at 2019-07-10 11:05:00+00:00

FELIPE had interactions at the following times: ['2019-06-13 09:50:00', '2019-06-13 09:51:00', '2019-06-13 09:52:00', '2019-06-13 09:53:00', '2019-06-13 09:54:00', '2019-06-13 10:12:00', '2019-06-13 10:21:00', '2019-06-13 10:43:00', '2019-06-13 10:56:00', '2019-06-13 10:57:00', '2019-06-13 11:00:00', '2019-06-13 14:50:00', '2019-06-13 14:51:00', '2019-06-13 14:54:00', '2019-06-13 15:32:00', '2019-06-13 15:33:00', '2019-06-13 15:36:00', '2019-06-13 15:44:00', '2019-06-13 16:03:00', '2019-06-13 16:04:00', '2019-06-13 16:06:00', '2019-06-13 16:24:00', '2019-06-14 09:20:00', '2019-06-14 09:29:00', '2019-06-14 10:06:00', '2019-06-14 10:19:00', '2019-06-14 10:32:00', '2019-06-14 10:33:00', '2019-06-14 10:35:00', '2019-06-14 14:41:00', '2019-06-14 14:47:00', '2019-06-14 14:48:00', '2019-06-14 14:49:00', '2019-06-14 14:51:00', '2019-06-14 15:08:00', '2019-06-14 15:20:00', '2019-06-14 15:22:00', '2019-06-14 15:29:00', '2019-06-14 15:31:00', '2019-06-14 15:32:00', '2019-06-17 11:02:00', '2019-06-17 11:04:00', '2019-06-17 12:49:00', '2019-06-17 14:01:00', '2019-06-17 14:02:00', '2019-06-18 10:25:00', '2019-06-18 10:36:00', '2019-06-18 10:40:00', '2019-06-18 11:01:00', '2019-06-18 13:42:00', '2019-06-18 13:44:00', '2019-06-18 13:45:00', '2019-06-19 08:56:00', '2019-06-19 08:58:00', '2019-06-19 08:59:00', '2019-06-19 09:11:00', '2019-06-19 09:12:00', '2019-06-19 09:14:00', '2019-06-19 09:26:00', '2019-06-19 09:31:00', '2019-06-19 09:42:00', '2019-06-19 10:25:00', '2019-06-19 10:26:00', '2019-06-19 10:27:00', '2019-06-19 11:25:00', '2019-06-19 12:01:00', '2019-06-19 12:05:00', '2019-06-19 12:06:00', '2019-06-19 12:23:00', '2019-06-19 12:34:00', '2019-06-19 12:43:00', '2019-06-19 12:48:00', '2019-06-19 12:49:00', '2019-06-19 12:50:00', '2019-06-20 10:05:00', '2019-06-20 10:15:00', '2019-06-20 10:36:00', '2019-06-20 10:51:00', '2019-06-20 11:17:00', '2019-06-20 14:56:00', '2019-06-20 14:57:00', '2019-06-20 14:59:00', '2019-06-20 15:08:00', '2019-06-20 15:09:00', '2019-06-20 15:20:00', '2019-06-20 15:27:00', '2019-06-20 15:30:00', '2019-06-20 15:55:00', '2019-06-21 09:43:00', '2019-06-21 10:38:00', '2019-06-21 10:39:00', '2019-06-21 11:11:00', '2019-06-21 11:14:00', '2019-06-21 11:39:00', '2019-06-21 11:40:00', '2019-06-21 11:46:00', '2019-06-21 11:47:00', '2019-06-21 11:48:00', '2019-06-21 11:49:00', '2019-06-21 12:03:00', '2019-06-21 12:41:00', '2019-06-21 12:57:00', '2019-06-21 13:00:00', '2019-06-21 13:01:00', '2019-06-21 13:02:00', '2019-06-24 10:54:00', '2019-06-24 10:56:00', '2019-06-24 10:57:00', '2019-06-24 10:58:00', '2019-06-24 10:59:00', '2019-06-24 11:01:00', '2019-06-24 11:08:00', '2019-06-24 11:09:00', '2019-06-24 11:14:00', '2019-06-24 11:22:00', '2019-06-24 15:40:00', '2019-06-24 15:41:00', '2019-06-24 15:42:00', '2019-06-24 15:43:00', '2019-06-24 16:09:00', '2019-06-24 16:11:00', '2019-06-25 10:25:00', '2019-06-25 10:30:00', '2019-06-25 10:48:00', '2019-06-25 10:49:00', '2019-06-25 11:13:00', '2019-06-25 11:14:00', '2019-06-25 11:26:00', '2019-06-25 15:11:00', '2019-06-25 15:17:00', '2019-06-25 15:52:00', '2019-06-25 15:54:00', '2019-06-25 16:03:00', '2019-06-25 16:04:00', '2019-06-25 16:09:00', '2019-06-26 09:10:00', '2019-06-26 09:11:00', '2019-06-26 09:32:00', '2019-06-26 09:33:00', '2019-06-26 09:53:00', '2019-06-26 09:54:00', '2019-06-26 09:58:00', '2019-06-26 10:05:00', '2019-06-26 10:06:00', '2019-06-26 10:37:00', '2019-06-26 10:39:00', '2019-06-26 13:22:00', '2019-06-26 13:35:00', '2019-06-26 13:39:00', '2019-06-26 13:40:00', '2019-06-26 14:17:00', '2019-06-26 14:18:00', '2019-06-27 09:31:00', '2019-06-27 09:37:00', '2019-06-27 12:29:00', '2019-06-27 12:46:00', '2019-06-27 13:03:00', '2019-06-27 13:49:00', '2019-06-27 13:52:00', '2019-06-27 13:53:00', '2019-06-28 10:15:00', '2019-06-28 10:16:00', '2019-06-28 10:17:00', '2019-06-28 10:18:00', '2019-06-28 10:19:00', '2019-06-28 10:20:00', '2019-06-28 11:12:00', '2019-06-28 11:13:00', '2019-07-01 08:44:00', '2019-07-01 08:46:00', '2019-07-01 08:49:00', '2019-07-01 08:51:00', '2019-07-01 09:06:00', '2019-07-01 09:21:00', '2019-07-01 10:13:00', '2019-07-01 13:20:00', '2019-07-01 14:45:00', '2019-07-02 08:52:00', '2019-07-02 13:32:00', '2019-07-02 13:33:00', '2019-07-02 13:34:00', '2019-07-02 13:35:00', '2019-07-02 13:36:00', '2019-07-02 14:35:00', '2019-07-02 14:39:00', '2019-07-03 09:27:00', '2019-07-03 09:39:00', '2019-07-03 09:41:00', '2019-07-03 10:14:00', '2019-07-03 10:15:00', '2019-07-03 10:16:00', '2019-07-03 10:17:00', '2019-07-03 10:18:00', '2019-07-03 10:54:00', '2019-07-03 11:16:00', '2019-07-03 11:17:00', '2019-07-03 11:37:00', '2019-07-03 12:03:00', '2019-07-03 12:05:00', '2019-07-04 09:30:00', '2019-07-04 09:31:00', '2019-07-04 09:39:00', '2019-07-04 09:45:00', '2019-07-04 09:46:00', '2019-07-04 10:00:00', '2019-07-04 10:25:00', '2019-07-04 10:55:00', '2019-07-04 10:59:00', '2019-07-04 14:02:00', '2019-07-04 14:04:00', '2019-07-04 14:05:00', '2019-07-04 14:30:00', '2019-07-04 14:38:00', '2019-07-04 15:09:00', '2019-07-04 15:19:00', '2019-07-05 10:10:00', '2019-07-05 10:12:00', '2019-07-05 10:13:00', '2019-07-05 10:20:00', '2019-07-05 10:46:00', '2019-07-05 11:05:00', '2019-07-05 11:40:00', '2019-07-05 11:41:00', '2019-07-05 11:57:00', '2019-07-05 12:57:00', '2019-07-05 13:02:00', '2019-07-05 13:07:00', '2019-07-05 13:13:00', '2019-07-08 11:34:00', '2019-07-08 11:39:00', '2019-07-08 11:40:00', '2019-07-08 11:46:00', '2019-07-08 11:50:00', '2019-07-08 12:37:00', '2019-07-08 14:32:00', '2019-07-08 14:33:00', '2019-07-08 15:42:00', '2019-07-08 15:48:00', '2019-07-08 15:51:00', '2019-07-09 10:57:00', '2019-07-09 10:59:00', '2019-07-09 11:01:00', '2019-07-09 11:17:00', '2019-07-09 11:20:00', '2019-07-09 13:37:00', '2019-07-09 13:38:00', '2019-07-09 13:39:00', '2019-07-09 13:40:00', '2019-07-09 14:34:00', '2019-07-09 14:36:00', '2019-07-09 14:37:00', '2019-07-10 10:00:00', '2019-07-10 10:06:00', '2019-07-10 10:14:00', '2019-07-10 10:30:00', '2019-07-10 10:44:00', '2019-07-10 11:05:00']

Neighbours, edges and paths#

To investigate who a node is connected with we can ask for its degree(), edges, or neighbours. As Raphtory graphs are directed, all of these functions also have an in_ and out_ variation, allowing you get only incoming and outgoing connections respectively. These functions return the following:

degree: A count of the number of unique connections a node has
edges: An Edges iterable of edge objects, one for each unique (src,dst) pair
neighbours: A PathFromNode iterable of node objects, one for each entity the original node shares an edge with

In the code below we call a selection of these functions to show the sort of questions you may ask.

Info

The final section of the code makes use of v.neighbours.name.collect() - this is a chain of functions which are run on each node in the PathFromNode iterable. We will discuss these sort of operations more in Chaining functions.

Python

import pandas as pd
from raphtory import Graph
from datetime import datetime

edges_df = pd.read_csv(
    "../data/OBS_data.txt", sep="\t", header=0, usecols=[0, 1, 2, 3, 4], parse_dates=[0]
)
edges_df["DateTime"] = pd.to_datetime(edges_df["DateTime"])
edges_df.dropna(axis=0, inplace=True)
edges_df["Weight"] = edges_df["Category"].apply(
    lambda c: 1 if (c == "Affiliative") else (-1 if (c == "Agonistic") else 0)
)

g = Graph()
g.load_edges_from_pandas(
    df=edges_df,
    src="Actor",
    dst="Recipient",
    time="DateTime",
    layer_col="Behavior",
    properties=["Weight"],
) 

v = g.node("FELIPE")
v_name = v.name
in_degree = v.in_degree()
out_degree = v.out_degree()
in_edges = v.in_edges
neighbours = v.neighbours
neighbour_names = v.neighbours.name.collect()

print(
    f"{v_name} has {in_degree} incoming interactions and {out_degree} outgoing interactions.\n"
)
print(in_edges)
print(neighbours, "\n")
print(f"{v_name} interacted with the following baboons {neighbour_names}")

Output

FELIPE has 17 incoming interactions and 18 outgoing interactions.

Edges(Edge(source=MALI, target=FELIPE, earliest_time=1561117140000, latest_time=1562753160000, properties={Weight: 1}, layer(s)=[Grooming, Resting, Presenting]), Edge(source=LOME, target=FELIPE, earliest_time=1560421260000, latest_time=1562149080000, properties={Weight: 1}, layer(s)=[Grooming, Resting, Playing with, Presenting, Chasing]), Edge(source=NEKKE, target=FELIPE, earliest_time=1560443040000, latest_time=1562596380000, properties={Weight: 1}, layer(s)=[Touching, Resting, Presenting, Grunting-Lipsmacking, Embracing]), Edge(source=PETOULETTE, target=FELIPE, earliest_time=1561628220000, latest_time=1562252940000, properties={Weight: 1}, layer(s)=[Resting]), Edge(source=EWINE, target=FELIPE, earliest_time=1560523260000, latest_time=1562585640000, properties={Weight: 1}, layer(s)=[Grooming, Resting, Presenting, Avoiding]), Edge(source=ANGELE, target=FELIPE, earliest_time=1560419400000, latest_time=1562753640000, properties={Weight: 1}, layer(s)=[Grooming, Resting, Presenting, Grunting-Lipsmacking, Submission, Copulating]), Edge(source=VIOLETTE, target=FELIPE, earliest_time=1560439920000, latest_time=1561373760000, properties={Weight: 1}, layer(s)=[Grooming, Resting, Presenting]), Edge(source=BOBO, target=FELIPE, earliest_time=1560423360000, latest_time=1561543080000, properties={Weight: -1}, layer(s)=[Presenting, Grunting-Lipsmacking, Avoiding]), Edge(source=MAKO, target=FELIPE, earliest_time=1560937320000, latest_time=1562679600000, properties={Weight: 1}, layer(s)=[Grooming, Resting, Playing with, Presenting, Grunting-Lipsmacking, Embracing]), Edge(source=FEYA, target=FELIPE, earliest_time=1560853500000, latest_time=1562586000000, properties={Weight: 1}, layer(s)=[Resting, Presenting]), ...)
PathFromNode(Node(name=MALI, earliest_time=1560422040000, latest_time=1562755320000), Node(name=LOME, earliest_time=1560419520000, latest_time=1562756100000), Node(name=NEKKE, earliest_time=1560419520000, latest_time=1562756700000), Node(name=PETOULETTE, earliest_time=1560422520000, latest_time=1562754420000), Node(name=EWINE, earliest_time=1560442020000, latest_time=1562754600000), Node(name=ANGELE, earliest_time=1560419400000, latest_time=1562754600000), Node(name=VIOLETTE, earliest_time=1560423600000, latest_time=1562754900000), Node(name=BOBO, earliest_time=1560419520000, latest_time=1562755500000), Node(name=MAKO, earliest_time=1560421620000, latest_time=1562756100000), Node(name=FEYA, earliest_time=1560420000000, latest_time=1562756040000), ...) 

FELIPE interacted with the following baboons ['MALI', 'LOME', 'NEKKE', 'PETOULETTE', 'EWINE', 'ANGELE', 'VIOLETTE', 'BOBO', 'MAKO', 'FEYA', 'LIPS', 'ATMOSPHERE', 'FANA', 'MUSE', 'HARLEM', 'PIPO', 'ARIELLE', 'SELF']