A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2021; you can also visit <a rel="external noopener" href="https://res.mdpi.com/d_attachment/applsci/applsci-11-02371/article_deploy/applsci-11-02371.pdf">the original URL</a>. The file type is <code>application/pdf</code>.
Structural Adversarial Variational Auto-Encoder for Attributed Network Embedding
<span title="2021-03-07">2021</span>
<i title="MDPI AG">
<a target="_blank" rel="noopener" href="https://fatcat.wiki/container/smrngspzhzce7dy6ofycrfxbim" style="color: black;">Applied Sciences</a>
</i>
Preserved Fulltext
As most networks come with some content in each node, attributed network embedding has aroused much research interest. Most existing attributed network embedding methods aim at learning a fixed representation for each node encoding its local proximity. However, those methods usually neglect the global information between nodes distant from each other and distribution of the latent codes. We propose Structural Adversarial Variational Graph Auto-Encoder (SAVGAE), a novel framework which encodes
<span class="external-identifiers">
<a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.3390/app11052371">doi:10.3390/app11052371</a>
<a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/lww3aaqmgvhwdab6he4erk46wy">fatcat:lww3aaqmgvhwdab6he4erk46wy</a>
</span>
more »
... e network structure and node content into low-dimensional embeddings. On one hand, our model captures the local proximity and proximities at any distance of a network by exploiting a high-order proximity indicator named Rooted Pagerank. On the other hand, our method learns the data distribution of each node representation while circumvents the side effect its sampling process causes on learning a robust embedding through adversarial training. On benchmark datasets, we demonstrate that our method performs competitively compared with state-of-the-art models.
<a target="_blank" rel="noopener" href="https://web.archive.org/web/20210309010103/https://res.mdpi.com/d_attachment/applsci/applsci-11-02371/article_deploy/applsci-11-02371.pdf" title="fulltext PDF download" data-goatcounter-click="serp-fulltext" data-goatcounter-title="serp-fulltext">
<button class="ui simple right pointing dropdown compact black labeled icon button serp-button">
<i class="icon ia-icon"></i>
Web Archive
[PDF]
<div class="menu fulltext-thumbnail">
<img src="https://blobs.fatcat.wiki/thumbnail/pdf/9c/70/9c7063aa5de7ae76f8dec680b0e766c3ecf93682.180px.jpg" alt="fulltext thumbnail" loading="lazy">
</div>
</button>
</a>
<a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.3390/app11052371">
<button class="ui left aligned compact blue labeled icon button serp-button">
<i class="unlock alternate icon" style="background-color: #fb971f;"></i>
mdpi.com
</button>
</a>