Effective Graph and Rank-based Contextual Embeddings for Textual and Multimedia Data

2026-08-28

AI Summary

为解决图嵌入方法高计算成本及维度不可解释性问题，提出GRaCE框架，利用基于排名的方法选择代表性节点子集以生成可解释的嵌入，在检索、分类和聚类任务上表现优异。

Abstract

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 step being the selection of a representative subset of nodes to provide interpretability for its dimensions and improve retrieval tasks. Despite its potential, RaDE's original proposal did not fully explore the effectiveness of representative subset selection across different classes or evaluate embeddings in tasks like classification and clustering. Inspired by RaDE, this work introduces GRaCE (Graph and Rank-based Contextual Embeddings), a fully unsupervised framework that generates interpretable embeddings by leveraging robust rank-based measures for representative subset selection and node embedding. GRaCE surpasses RaDE and Original Features across diverse datasets, including textual and image collections, excelling in retrieval, classification, and clustering tasks, considering state-of-the-art Transformer models as feature descriptors and Graph Convolutional Networks models in classification tasks.

Problem

• graph embedding
• computational cost
• interpretability
• retrieval
• classification

Innovation

• Graph and Rank-based Contextual Embeddings
• Unsupervised Framework
• Representative Subset Selection
• Interpretable Embeddings
• Robust Rank-based Measures