Multi-order graph clustering with adaptive node-level weight learning

Ye Liu, Xuelei Lin, Yejia Chen, Reynold Cheng

Abstract: Highlights•We propose a multi-order graph clustering model to integrate multiple higher-order and lower-order structures at the node level.•An adaptive weight learning mechanism is formulated to automatically adjust the contributions of different motifs for each node.•We employ the model to solve fragmentation issue and achieve more accurate partition results simultaneously.•Extensive experiments show the outperformance of our proposed method.