Graph Contrastive Learning (GCL) is a popular self-supervised learning (SSL) technique. However, mainstream GCL methods usually favor single fine-grained random augmentation schemes, which will destroy the structural integrity of the graph, and they largely ignore the topology of the graph structure, that is, multi-granularity characteristics. Graphs are typically composed of homogeneous regions with varying granularities, where nodes within a region exhibit strong homogeneous properties. However, most of the real graphs are heterogeneous, and in the local regions of heterogeneous graphs, interconnected nodes may have similar semantic information, even if they do not belong to the same class. To this end, we propose a new multi-granularity graph contrastive learning framework via granular-ball (GBGCL) to explore the potential on heterogeneous graphs. Specifically, we develop an adaptive granular-ball augmentation strategy that identifies multi-granularity homogeneous regions in the heterogeneous graph, and treat nodes in the same granular-ball as positive pairs, while nodes in different granular-balls as negative pairs. In addition, we integrate the feature information of the nodes in original graph and semantic information of similar nodes in the feature space. Node representations are obtained through joint optimization of losses. Experiments on heterogeneous graphs demonstrate the unique advantages of our framework.
Shuyin Xia, Guan Wang, Cheng Tan et al.· IEEE Transactions on Pattern...· 0 citations
Hypergraph representation learning aims to capture high-order information in graphs by constructing hyperedges that simultaneously connect multiple nodes. These hyperedges adapt to the graph's topological features, facilitating the extraction of high-order relationships at multiple granularities. Most prior work relies on predefined definitions to generate hyperedges, overlooking the diversity in graph topological structures and the multi-granularity characteristics of hyperedges. As a result, this limits their ability to effectively and adaptively discover high-order relationships and efficiently process complex structural information. To address this limitation, we propose a novel framework called \underline{M}ulti-\underline{G}ranularity \underline{H}ypergraph \underline{R}epresentation \underline{L}earning (MGHRL). MGHRL introduces an Adaptive Granular Hypergraph Generation strategy, which generates hyperedges at multiple levels of granularity through the adaptive splitting of granular-ball, effectively capturing high-order relationships based on the graph's topological structure. Additionally, we propose a Multi-Granularity Hypergraph Network with multiple sub-networks, capturing features from hyperedges at different granularities and integrating them via hierarchical reversible connections. Experimental results show that MGHRL significantly outperforms baseline models on benchmark datasets.
Sen Zhao, Yi-Fan Guan, Jinyuan Ni et al.· 0 citations
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