WoMG is a Python library for Word-of-Mouth Cascades Generation which combines topic modeling and a topic-aware propagation model to create realistic information-rich cascades, whose shape depends on many factors, including the topic of theitem and its virality, the homophily of the social network, the interests of its users and their social influence.

WoMG starts with any (un)directed, (un)weighted graph and a collection of documents, generates interests of each node, extracts the topic distribution of each document, and finally outputs the propagation DAGs (Directed-Acyclic graph) of the documents through the network.

• click==7.1.2
• decorator==4.4.2
• joblib==1.0.1
• networkx==2.5
• numpy==1.19.5
• scikit-learn==0.24.1
• scipy==1.5.4
• tqdm==4.56.2
• gensim==3.8.3

Install using pip:

$ pip install womg

$ git clone https://github.com/FedericoCinus/WoMG.git

The WoMG package provides a Python module and a command-line executable.
1.

from womg import womg
import networkx as nx

N = 100
graph = nx.random_geometric_graph(N, .2)
docs_path = '/this/is/an/example/documents_folder/'

prop = womg(graph=graph, docs_path=docs_path)

prop.propagations[0] # [(time, node)]

[(0,0), (0,2), (1,5) .. ]

$ womg --docs_folder 'example/documents_folder/' --graph 'example/graph_folder/graph_edgelist.txt'

The demo notebook provides a tutorial.

You can check out the other options available to use with WoMG from jupyter notebook using:

?womg

or in the command-line typing:

$ womg --help

numb_topics defines the dimension of topic space where documents and nodes are described
homophily modifies the interests similarity between connected nodes
gn_strength modifies the strength of influence exerted on each node at the initial time step
infl_strength increments the strength of influence exerted on each node with the interests of
virality_resistance determines the global resistance to propagation of the documents

This repository provides a reference implementation of WoMG as described in:

WoMG: a Library for Word-of-Mouth Cascades Generation.
Federico Cinus, Francesco Bonchi, Corrado Monti, André Panisson.
https://dl.acm.org/doi/abs/10.1145/3437963.3441693

Generating realistic interest-driven information cascades.
Federico Cinus, Francesco Bonchi, Corrado Monti, André Panisson.
https://ojs.aaai.org//index.php/ICWSM/article/view/7283

@inproceedings{cinus2020generating, title={Generating Realistic Interest-Driven Information Cascades}, author={Cinus, Federico and Bonchi, Francesco and Monti, Corrado and Panisson, Andr{'e}}, booktitle={Proceedings of the International AAAI Conference on Web and Social Media}, volume={14}, pages={107--118}, year={2020} }

@inproceedings{cinus2021womg, title={WoMG: A Library for Word-of-Mouth Cascades Generation}, author={Cinus, Federico and Bonchi, Francesco and Monti, Corrado and Panisson, Andr{'e}}, booktitle={Proceedings of the 14th ACM International Conference on Web Search and Data Mining}, pages={1065--1068}, year={2021} }