Dual-channel attribute graph clustering beyond the homogeneity assumption

In recent years, significant progress has been made in the research of attribute graph clustering. However, existing methods are mostly based on the homogeneity assumption, thereby neglecting the application scenarios of heterogeneous graphs, leading to the loss of high-frequency information and poor clustering results during the clustering process. To address this issue, a novel dual-channel attribute graph clustering (DCAGC) method was proposed. A mixture of Gaussian models was used to predict the homogeneity of node connections and two views of homogeneous and heterogeneous were built, based on this prediction to capture low-frequency and high-frequency information in the graph from different perspectives. Simultaneously, by integrating contrastive learning and clustering, more precise node embeddings were achieved. Compared to other methods, DCAGC demonstrates significant clustering performance when handling heterogeneous graph datasets and exhibits strong resilience to anomalous connections.