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2.4. Multimodal Fusion Strategies

Recent works (e.g., MMF [Li et al., 2023]) employ cross‑modal attention, but they target limited‑scale datasets (≤ 1 M pages). Product: Sevina Model Webeweb Set 45rar Exclusive Rating:

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Key Features of the Sevina Model

CMA₁: Query = hᵍ,   Key/Value = hᵛ, hᵗ
CMA₂: Query = hᵛ,   Key/Value = hᵍ, hᵗ
CMA₃: Query = hᵗ,   Key/Value = hᵍ, hᵛ

3.2. Graph‑Transformer Encoder (GTE)

  1. Graph Construction – Each page is a node v. An undirected edge (v, u) exists if either page links to the other. Edge weights are derived from anchor‑text similarity.
  2. Neighborhood Sampling – For scalability, we adopt Layer‑wise Sampling (as in GraphSAGE) with a fan‑out of 25 for the first three layers and 10 for deeper layers.
  3. Positional Encoding – We compute Graph‑Distance Encoding (GDE): a sinusoidal function of the shortest‑path distance to a set of K = 128 landmark nodes selected by PageRank.
  4. Transformer Layers – Each layer consists of multi‑head self‑attention (8 heads), followed by a feed‑forward network (FFN) with hidden size 2048. LayerNorm and residual connections are applied as usual.
  5. Output – Node embeddings hᵍᵥ ∈ ℝ⁵¹².