Effective Graph and Rank-based Contextual Embeddings for Textual and Multimedia Data
In a data-driven world, efficiently organizing and mapping relationships between objects is crucial. Graphs are powerful tools for modeling these connections, being widely used in social networks, telecommunications, and biology. However, graph-based methods often face high computational costs, particularly in memory and space usage. To address this, graph embedding techniques, also referred to as Network Representation Learning, encode graph information into lower-dimensional representations while preserving structural aspects. Traditional methods, however, lack interpretable dimensions. RaDE (Rank Diffusion Embedding) introduces a new approach using rank-based information, with a key st...
| Authors | Thiago César Castilho Almeida, Gustavo Rosseto Letício, Lucas Pascotti Valem et al. |
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| Primary category | cs.LG |
| Published | 29 August 2026 |
| Last updated at source | 23 September 2026 |
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