Ecosyste.ms: Awesome

An open API service indexing awesome lists of open source software.

Awesome Lists | Featured Topics | Projects

https://github.com/ferencberes/online-node2vec

Node Embeddings in Dynamic Graphs
https://github.com/ferencberes/online-node2vec

embeddings network-embedding representation-learning research temporal-networks twitter-data twitter-data-analysis twitter-dataset

Last synced: about 1 month ago
JSON representation

Node Embeddings in Dynamic Graphs

Host: GitHub
URL: https://github.com/ferencberes/online-node2vec
Owner: ferencberes
License: cc0-1.0
Created: 2018-09-17T09:00:12.000Z (over 6 years ago)
Default Branch: master
Last Pushed: 2022-01-12T09:09:28.000Z (almost 3 years ago)
Last Synced: 2024-04-26T02:21:56.694Z (8 months ago)
Topics: embeddings, network-embedding, representation-learning, research, temporal-networks, twitter-data, twitter-data-analysis, twitter-dataset
Language: Jupyter Notebook
Homepage: https://appliednetsci.springeropen.com/articles/10.1007/s41109-019-0169-5
Size: 2.84 MB
Stars: 53
Watchers: 5
Forks: 12
Open Issues: 0
Metadata Files:
- Readme: README.md
- License: LICENSE

Awesome Lists containing this project

README

        Online Node2Vec

===============

![build](https://github.com/ferencberes/online-node2vec/actions/workflows/main.yml/badge.svg)

[![codecov](https://codecov.io/gh/ferencberes/online-node2vec/branch/master/graph/badge.svg?token=H6RRUKXQRF)](https://codecov.io/gh/ferencberes/online-node2vec)

![PyPI - Python Version](https://img.shields.io/badge/python-3.6%20|%203.7%20|%203.8%20|%203.9-blue.svg)

[![Binder](https://mybinder.org/badge_logo.svg)](https://mybinder.org/v2/gh/ferencberes/online-node2vec/HEAD?filepath=ipython%2FOnlineNodeEmbeddings.ipynb)

This repository contains the code related to the research of [Ferenc Béres](https://github.com/ferencberes), [Róbert Pálovics](https://github.com/rpalovics), [Domokos Miklós Kelen](https://github.com/proto-n) and [András A. Benczúr](https://mi.nemzetilabor.hu/people/andras-benczur).

# Introduction

We propose two online node embedding models (StreamWalk and online second order similarity) for temporally evolving networks. Two nodes are required to be mapped close in the vector space whenever they lie on short paths formed by recent edges in the first model, and whenever the set of their recent neighbors is similar in the second model.

Please cite our [paper](https://appliednetsci.springeropen.com/articles/10.1007/s41109-019-0169-5) if you use our work:

```

@Article{Béres2019,

author="B{\'e}res, Ferenc

and Kelen, Domokos M.

and P{\'a}lovics, R{\'o}bert

and Bencz{\'u}r, Andr{\'a}s A.",

title="Node embeddings in dynamic graphs",

journal="Applied Network Science",

year="2019",

volume="4",

number="64",

pages="25",

}

```

I presented a former version of our work at the 7th International Conference on Complex Networks and Their Applications that is availabe on this [branch](https://github.com/ferencberes/online-node2vec/tree/complex_networks_2018).

# Data

**US Open 2017 (UO17)** and **Roland-Garros 2017 (RG17)** Twitter datasets were published in our [previous work](https://link.springer.com/article/10.1007/s41109-018-0080-5) for the first time. Please cite this article if you use our data sets in your research:

```

@Article{Béres2018,

author="B{\'e}res, Ferenc

and P{\'a}lovics, R{\'o}bert

and Ol{\'a}h, Anna

and Bencz{\'u}r, Andr{\'a}s A.",

title="Temporal walk based centrality metric for graph streams",

journal="Applied Network Science",

year="2018",

volume="3",

number="32",

pages="26",

}

```

These Twitter datasets are available on the [website](https://dms.sztaki.hu/~fberes/tennis/) of our research group. In order to process the data you need to install the [twittertennis](https://github.com/ferencberes/twittertennis) Python package. It will automatically [download and prepare](scripts/preprocess_data.py) the datasets for you.

# Install

```bash

pip install online-node2vec

```

# Usage

After installing every requirement execute the following script to run both node representation learning and evaluation for the similarity search task.

```bash

cd scripts

bash run.sh

```

The major steps in our pipeline are:

   * [Download and preprocess](scripts/preprocess_data.py) data

   * Learning [StreamWalk](scripts/streamwalk_runner.py) representations

   * Learning [Second order similarity](scripts/second_order_runner.py) representations

   * [Evaluate](scripts/evaluate.py) node embeddings for the similarity search supervised experiment