Adaptive Graph Convolution Using Heat Kernel for Attributed Graph Clustering

Abstract

Attributed graphs contain a lot of node features and structural relationships, and how to utilize their inherent information sufficiently to improve graph clustering performance has attracted much attention. Although existing advanced methods exploit graph convolution to capture the global structure of an attributed graph and achieve obvious improvements for clustering results, they cannot determine the optimal neighborhood that reflects the relevant information of connected nodes in a graph. To address this limitation, we propose a novel adaptive graph convolution using a heat kernel model for attributed graph clustering (AGCHK), which exploits the similarity among nodes under heat diffusion to flexibly restrict the neighborhood of the center node and enforce the graph smoothness. Additionally, we take the Davies–Bouldin index (DBI) instead of the intra-cluster distance individually as the selection criterion to adaptively determine the order of graph convolution. The clustering results of AGCHK on three benchmark datasets—Cora, Citeseer, and Pubmed—are all more than 1% higher than the current advanced model AGC, and 12% on the Wiki dataset especially, which obtains a state-of-the-art result in the task of attributed graph clustering.

An Improved Evolutionary Multi-Objective Clustering Algorithm Based on Autoencoder

“…However, the dimension reduction of the input datasets has rarely been considered. For this issue, autoencoder as a data dimension reduction method based on deep learning has gained popularity in clustering and helps to obtain good clustering accuracy especially on high-dimensional datasets [14][15][16][17][18][19][20]. It can maintain the nonlinear feature of the datasets while reducing the dimensionality.…”

“…Then, the feature representation was clustered by k-means, which could significantly improve the accuracy of clustering. The following studies have focused on the expressions of the autoencoder's loss functions and the clustering algorithms used [15][16][17][18][19][20]. Thus, EMO-KC as a clustering algorithm is expected to obtain better clustering accuracy when combined with the autoencoder.…”