Summary of Bipartite Graph Variational Auto-encoder with Fair Latent Representation to Account For Sampling Bias in Ecological Networks, by Emre Anakok et al.
Bipartite Graph Variational Auto-Encoder with Fair Latent Representation to Account for Sampling Bias in Ecological Networks
by Emre Anakok, Pierre Barbillon, Colin Fontaine, Elisa Thebault
First submitted to arxiv on: 4 Mar 2024
Categories
- Main: Machine Learning (stat.ML)
- Secondary: Machine Learning (cs.LG); 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 The proposed method represents bipartite networks using graph embeddings tailored for studying ecological networks. The variational graph auto-encoder approach is adapted for the bipartite case, generating embeddings in a latent space based on node connection probabilities. To address sampling bias, the fairness framework from sociology is translated to ecology and incorporates the Hilbert-Schmidt independence criterion (HSIC) as an additional penalty term. This ensures the structure of the latent space remains independent of continuous variables related to the sampling process. The approach is applied to the Spipoll data set, a citizen science monitoring program prone to sampling bias. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary We propose a new way to understand relationships between plants and pollinators. Our method uses math to look at big networks where many things are connected. We want to make sure our results aren’t biased by how we collected the data. To do this, we use a special kind of math that helps control for sampling bias. This new approach can change what we know about these important ecological networks. |
Keywords
* Artificial intelligence * Encoder * Latent space
