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<!DOCTYPE html>
<html>
<head>
<meta charset="utf-8">
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<meta content="Sequential Signal Mixing Aggregation for Message Passing Graph Neural Networks" property="og:title"/>
<meta content="https://almogdavid.github.io/SSMA/" property="og:url"/>
<!-- Keywords for your paper to be indexed by-->
<meta content="GNN, MPGNN, Graph Neural Networks, Message Passing Graph Neural Networks"
name="keywords">
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<title>Sequential Signal Mixing Aggregation for Message Passing Graph Neural Networks</title>
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<h1 class="title is-1 publication-title">Sequential Signal Mixing Aggregation for Message Passing
Graph Neural Networks</h1>
<div class="is-size-5 publication-authors">
<!-- Paper authors -->
<span class="author-block">
<a href="mailto:butovsky.mitchell@gmail.com" target="_blank">Mitchell Keren Taraday [1]</a><sup>*</sup>,</span>
<span class="author-block">
<a href="mailto:almogdavid@gmail.com" target="_blank"> Almog David [1]</a><sup>*</sup>,</span>
<span class="author-block">
<a href="mailto:chaimbaskin@bgu.ac.il" target="_blank">Chaim Baskin [2]</a>
</span>
</div>
<div class="is-size-5 publication-authors">
<span class="author-block">[1] - Department of Computer Science, Technion<br>[2] - School of Electrical and Computer Engineering Ben-Gurion University of the Negev<br>NeurIPS 2024, the Thirty-Eighth Annual Conference on Neural Information Processing Systems</span>
<span class="eql-cntrb"><small><br><sup>*</sup>Indicates Equal Contribution</small></span>
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href="static/pdf/SSMA____presentation_NeurIPS2024.pdf"
target="_blank">
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<span>Presentation</span>
</a>
</span>
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<a class="external-link button is-normal is-rounded is-dark" href="https://arxiv.org/pdf/2409.19414"
target="_blank">
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<span>Paper</span>
</a>
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<h2 class="title is-3">Abstract</h2>
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<p>
Message Passing Graph Neural Networks (MPGNNs) have emerged as the pre-
ferred method for modeling complex interactions across diverse graph entities.
While the theory of such models is well understood, their aggregation module has
not received sufficient attention. Sum-based aggregators have solid theoretical foun-
dations regarding their separation capabilities. However, practitioners often prefer
using more complex aggregations and mixtures of diverse aggregations. In this
work, we unveil a possible explanation for this gap. We claim that sum-based aggre-
gators fail to "mix" features belonging to distinct neighbors, preventing them from
succeeding at downstream tasks. To this end, we introduce Sequential Signal Mix-
ing Aggregation (SSMA), a novel plug-and-play aggregation for MPGNNs. SSMA
treats the neighbor features as 2D discrete signals and sequentially convolves them,
inherently enhancing the ability to mix features attributed to distinct neighbors.
By performing extensive experiments, we show that when combining SSMA with
well-established MPGNN architectures, we achieve substantial performance gains
across various benchmarks, achieving new state-of-the-art results in many settings.
</p>
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<img alt="Method" src="method_fig.png"/>
<h2 class="subtitle has-text-centered">
Method overview
</h2>
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Results
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Results cont'd
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<h2 class="title">BibTeX</h2>
<pre><code>@misc{taraday2024sequentialsignalmixingaggregation,
title={Sequential Signal Mixing Aggregation for Message Passing Graph Neural Networks},
author={Mitchell Keren Taraday and Almog David and Chaim Baskin},
year={2024},
eprint={2409.19414},
archivePrefix={arXiv},
primaryClass={cs.LG},
url={https://arxiv.org/abs/2409.19414},
}</code></pre>
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</section>
<!--End BibTex citation -->
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