GraphSAGE [Hamilton et al., 2017] and GAT [Veličković et al., 2018] have been applied to hyperlink graphs (e.g., LinkBERT [Sun et al., 2022]), but they lack multimodal fusion.

Recent works (e.g., MMF [Li et al., 2023]) employ cross‑modal attention, but they target limited‑scale datasets (≤ 1 M pages).

Sevina distinguishes itself by (i) scaling GTE to 45 M nodes via neighborhood sampling, (ii) jointly training vision, text, and graph streams, and (iii) providing exclusive task‑specific heads that leverage the fused representation.

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The body of the essay should expand on the details of the Sevina model within the Webeweb set.

| Task | Head Architecture | Loss | |----------|----------------------|----------| | Retrieval | Dual‑encoder: eᵥ (pages) vs. qᵣ (query encoder) | InfoNCE contrastive loss | | Recommendation | Seq2Seq Transformer (2 layers) taking eᵥ as context | Cross‑entropy over next‑page IDs | | Tagging | Fully‑connected (2 layers) with sigmoid activation | Binary cross‑entropy per label |

All heads share the base encoder weights; training is performed jointly with weighted loss coefficients (λᵣ = 0.5, λₙ = 0.3, λₜ = 0.2).