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Introduction and dataset

After reading data into Raphtory we can now make use of the graph representation to ask some interesting questions. For this tutorial, we will use a dataset from SocioPatterns, comprising different behavioural interactions between a group of 22 baboons over a month.

Info

If you want to read more about the dataset, you can check it out in this paper: V. Gelardi, J. Godard, D. Paleressompoulle, N. Claidière, A. Barrat, “Measuring social networks in primates: wearable sensors vs. direct observations”, Proc. R. Soc. A 476:20190737 (2020).

In the below code we load this dataset into a dataframe and do a small amount of preprocessing to prepare it for loading into Raphtory. This includes dropping rows with blank fields and mapping the values of the behaviour category into a weight which can be aggregated. The mapping consists of the following conversions:

  • Affiliative (positive interaction) → +1
  • Agonistic (negative interaction) → -1
  • Other (neutral interaction) → 0

Graph 

import pandas as pd

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)
)
print(edges_df.head())

Output

              DateTime   Actor Recipient  Behavior     Category  Weight
15 2019-06-13 09:50:00  ANGELE    FELIPE  Grooming  Affiliative       1
17 2019-06-13 09:50:00  ANGELE    FELIPE  Grooming  Affiliative       1
19 2019-06-13 09:51:00  FELIPE    ANGELE   Resting  Affiliative       1
20 2019-06-13 09:51:00  FELIPE      LIPS   Resting  Affiliative       1
21 2019-06-13 09:51:00  ANGELE    FELIPE  Grooming  Affiliative       1

Next we load this into Raphtory using the load_edges_from_pandas function, modelling it as a weighted multi-layer graph, with a layer per unique behaviour.

Graph 

import raphtory as rp

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

Output

Graph(number_of_nodes=22, number_of_edges=290, number_of_temporal_edges=3196, earliest_time=1560419400000, latest_time=1562756700000)