Graphs Get a Social Boost: Improving Link Prediction with Community Structure
![Community-level embeddings are constructed by first discerning graph communities, then identifying central nodes within each using PageRank centrality [latex] C_v(t) = (1-\alpha) + \alpha \sum_{u \in B(v)} \frac{C_u(t-1)}{d(u)} [/latex], after which prior probabilities address structural incompleteness, and finally, edge representations are refined by integrating local neighborhood details, path information, and cross-community relationships to improve link prediction performance.](https://arxiv.org/html/2512.21166v1/doc/Figure_2.png)
A new approach enhances graph representation learning by integrating community detection, leading to more accurate predictions of missing connections.
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