Summary of Signed Graph Autoencoder For Explainable and Polarization-aware Network Embeddings, by Nikolaos Nakis et al.
Signed Graph Autoencoder for Explainable and Polarization-Aware Network Embeddings
by Nikolaos Nakis, Chrysoula Kosma, Giannis Nikolentzos, Michalis Chatzianastasis, Iakovos Evdaimon, Michalis Vazirgiannis
First submitted to arxiv on: 16 Sep 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Social and Information Networks (cs.SI)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary This paper proposes the Signed Graph Archetypal Autoencoder (SGAAE) framework, which extracts node-level representations that express node memberships over distinct extreme profiles referred to as archetypes within a signed network. The SGAAE employs a recently proposed likelihood for analyzing signed networks based on the Skellam distribution, combined with relational archetypal analysis and Graph Neural Networks (GNNs). The paper demonstrates the capability of SGAAEs to successfully infer node memberships over different underlying latent structures while extracting competing communities formed through the participation of opposing views in the network. Additionally, it introduces the 2-level network polarization problem and shows how SGAAE characterizes such a setting. The proposed model achieves high performance in different tasks of signed link prediction across four real-world datasets, outperforming several baseline models. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper proposes a new AI model that helps understand complex networks by identifying groups or communities within them. It’s like trying to find patterns in a big messy graph. The model uses special math and computer programming to figure out what the different groups are doing and how they relate to each other. This is important because it can help us understand things like who likes whom, or which companies are working together. |
Keywords
» Artificial intelligence » Autoencoder » Likelihood
