Bridging the Gap between Community and Node Representations: Graph Embedding via Community Detection

TL;DR

This paper introduces DAOR, a fast, parameter-free graph embedding method that produces interpretable, high-quality embeddings suitable for various tasks without extensive tuning or high computational costs.

Contribution

The paper presents DAOR, a novel graph embedding technique that is efficient, parameter-free, and produces interpretable embeddings, outperforming existing methods in speed and comparable in quality.

Findings

DAOR is significantly faster than existing methods.

DAOR produces competitive embeddings for node classification.

DAOR generates interpretable and compact embeddings.

Abstract

Graph embedding has become a key component of many data mining and analysis systems. Current graph embedding approaches either sample a large number of node pairs from a graph to learn node embeddings via stochastic optimization or factorize a high-order proximity/adjacency matrix of the graph via computationally expensive matrix factorization techniques. These approaches typically require significant resources for the learning process and rely on multiple parameters, which limits their applicability in practice. Moreover, most of the existing graph embedding techniques operate effectively in one specific metric space only (e.g., the one produced with cosine similarity), do not preserve higher-order structural features of the input graph and cannot automatically determine a meaningful number of embedding dimensions. Typically, the produced embeddings are not easily interpretable, which complicates further analyses and limits their applicability. To address these issues, we propose DAOR, a highly efficient and parameter-free graph embedding technique producing metric space-robust, compact and interpretable embeddings without any manual tuning. Compared to a dozen state-of-the-art graph embedding algorithms, DAOR yields competitive results on both node classification (which benefits form high-order proximity) and link prediction (which relies on low-order proximity mostly). Unlike existing techniques, however, DAOR does not require any parameter tuning and improves the embeddings generation speed by several orders of magnitude. Our approach has hence the ambition to greatly simplify and speed up data analysis tasks involving graph representation learning.