Predicting Diffusion Reach Probabilities via Representation Learning on Social Networks
Furkan Gürsoy
Ahmet Onur Durahim
10.6084/m9.figshare.7565894.v1
https://imisc.figshare.com/articles/journal_contribution/Predicting_Diffusion_Reach_Probabilities_via_Representation_Learning_on_Social_Networks/7565894
<div>
<div>
<div>
<div>
<p><b>Abstract
</b></p>
<p>Diffusion reach probability between two nodes on a network is defined as the probability of a cascade originating from one
node reaching to another node. An infinite number of cascades would enable calculation of true diffusion reach
probabilities between any two nodes. However, there exists only a finite number of cascades and one usually has access
only to a small portion of all available cascades. In this work, we addressed the problem of estimating diffusion reach
probabilities given only a limited number of cascades and partial information about underlying network structure. Our
proposed strategy employs node representation learning to generate and feed node embeddings into machine learning
algorithms to create models that predict diffusion reach probabilities. We provide experimental analysis using synthetically
generated cascades on two real-world social networks. Results show that proposed method is superior to using values
calculated from available cascades when the portion of cascades is small.
</p><p><br></p><p>
</p><div>
<div>
<div>
<div>
<p><b>Editor:</b> H. Kemal İlter, Ankara Yıldırım Beyazıt University, Turkey<br><b>
Received:</b> August 19, 2018, <b>Accepted:</b> October 18, 2018, <b>Published:</b> November 10, 2018
</p>
<p><b>Copyright:</b> © 2018 IMISC Gürsoy, Durahim. This is an open-access article distributed under the terms of the Creative
Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the
original author and source are credited. </p>
</div>
</div>
</div>
</div>
</div>
</div>
</div>
</div>
2019-01-09 19:00:36
Social networks
imisc
Information diffusion
Representation learning
Influence maximization