simondelarue / TemporalRandomWalk

Given a temporal network, performs Temporal Random Walk using different sampling strategies.

Temporal Random Walk

Considering a temporal network, we implement a framework that computes temporal random walk according to some sampling strategy.

Temporal network

A temporal network is a set of nodes and timestamped edges that exist within a period of time . This graph can be considered as a sequence of interactions, where each interaction is an event that comes in the form of a triplet , where and represent nodes in and is the time at which these nodes interacted with each other.
Temporal networks are encoded as dictionaries, where each key is a node in the graph and each corresponding value is a dictionary that maps for each timestep, a list of nodes which interacted with the key.

Temporal random walk

A temporal random walk is defined as a sequence of nodes in a temporal network. Such a walk exists between two nodes, if one can find a sequence of edges with non-decreasing timesteps that connects these two nodes.

Table of contents

1. Setup
2. Usage
3. Datasets

1. Setup

Install dependencies

[~/]$ git clone https://github.com/simondelarue/TemporalRandomWalk.git
[~/]$ cd TemporalRandomWalk
[~/TemporalRandomWalk]$ python -m venv myvenv
[~/TemporalRandomWalk]$ source myvenv/bin/activate
(myvenv)[~/TemporalRandomWalk]$ pip install requirements.txt

2. Usage

Run python src/main.py using the following arguments to chose data, size of test graph or sampling strategy:

Arguments

--data              Data source {SF2H, HighSchool, ia-contact, ia-contacts_hypertext2009, fb-forum, ia-enron-employees}
--test_size        Size of test dataset (in proportion of total number of edges)
--strategy          Sampling strategy: \{linear, exponential, uniform\}    

Example

$ python src/main.py --data SF2H --test_size 0.1 --strategy linear

Device : cpu
Preprocessing data ...
Splitting data ...

Creating stream graphs ...

Train graph
# nodes : 403
# edges : 133874

Test graph
# nodes : 201
# edges : 6648

Start edge: (306, 87, 145080)

Temporal random walk: [306, 87, 306, 87, 306, 87, 199, 79, 199, 79, 199, 79, 133, 165, 79, 199, 79, 199, 79, 133]

3. Datasets

In this work we use real-world datasets, where interactions between entities are encoded through events in the form of triplets , where and represent nodes in the graph, and is the time at which these nodes interacted with each other. We use the following datasets:

Dataset
SF2H
HighSchool
ia-contact
ia-contacts-hypertext2009
ia-enron-employees