Adaptive Graph Encoder for Attributed Graph Embedding

TL;DR

This paper introduces AGE, an adaptive graph encoder that improves attributed graph embedding by effectively filtering noise and enhancing features, outperforming existing methods on clustering and link prediction tasks.

Contribution

The paper proposes a novel adaptive graph encoder framework that addresses limitations of GCN-based methods through Laplacian smoothing and iterative feature enhancement.

Findings

AGE outperforms state-of-the-art methods on benchmark datasets.

The Laplacian smoothing filter effectively reduces high-frequency noise.

Iterative feature strengthening improves node embeddings.

Abstract

Attributed graph embedding, which learns vector representations from graph topology and node features, is a challenging task for graph analysis. Recently, methods based on graph convolutional networks (GCNs) have made great progress on this task. However,existing GCN-based methods have three major drawbacks. Firstly,our experiments indicate that the entanglement of graph convolutional filters and weight matrices will harm both the performance and robustness. Secondly, we show that graph convolutional filters in these methods reveal to be special cases of generalized Laplacian smoothing filters, but they do not preserve optimal low-pass characteristics. Finally, the training objectives of existing algorithms are usually recovering the adjacency matrix or feature matrix, which are not always consistent with real-world applications. To address these issues, we propose Adaptive Graph Encoder (AGE), a novel attributed graph embedding framework. AGE consists of two modules: (1) To better alleviate the high-frequency noises in the node features, AGE first applies a carefully-designed Laplacian smoothing filter. (2) AGE employs an adaptive encoder that iteratively strengthens the filtered features for better node embeddings. We conduct experiments using four public benchmark datasets to validate AGE on node clustering and link prediction tasks. Experimental results show that AGE consistently outperforms state-of-the-art graph embedding methods considerably on these tasks.