Graph clustering is a crucial task in network analysis with widespread applications, focusing on partitioning nodes into distinct groups with stronger intra-group connections than inter-group ones. Recently, contrastive learning has achieved significant progress in graph clustering. However, most methods suffer from the following issues: 1) an over-reliance on meticulously designed data augmentation strategies, which can undermine the potential of contrastive learning. 2) overlooking cluster-oriented structural information, particularly the higher-order cluster(community) structure information, which could unveil the mesoscopic cluster structure information of the network. In this study, Structure-enhanced Contrastive Learning (SECL) is introduced to addresses these issues by leveraging inherent network structures. SECL utilizes a cross-view contrastive learning mechanism to enhance node embeddings without elaborate data augmentations, a structural contrastive learning module for ensuring structural consistency, and a modularity maximization strategy for harnessing clustering-oriented information. This comprehensive approach results in robust node representations that greatly enhance clustering performance. Extensive experiments on six datasets confirm SECL's superiority over current state-of-the-art methods, indicating a substantial improvement in the domain of graph clustering.

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Crowdsourcing truth inference aims to assign a correct answer to each task from candidate answers that are provided by crowdsourced workers. A common approach is to generate workers’ reliabilities to represent the quality of answers. Although crowdsourced triples can be converted into various crowdsourced relationships, the available related methods are not effective in capturing these relationships to alleviate the harm to inference that is caused by conflicting answers. In this research, we propose a Re liability-driven M ulti-view G raph E mbedding framework for T ruth i nference (TiReMGE), which explores multiple crowdsourced relationships by organically integrating worker reliabilities into a graph space that is constructed from crowdsourced triples. Specifically, to create an interactive environment, we propose a reliability-driven initialization criterion for initializing vectors of tasks and workers as interactive carriers of reliabilities. From the perspective of multiple crowdsourced relationships, a multi-view graph embedding framework is proposed for reliability information interaction on a task-worker graph, which encodes latent crowdsourced relationships into vectors of workers and tasks for reliability update and truth inference. A heritable reliability updating method based on the Lagrange multiplier method is proposed to obtain reliabilities that match the quality of workers for interaction by a novel constraint law. Our ultimate goal is to minimize the Euclidean distance between the encoded task vector and the answer that is provided by a worker with high reliability. Extensive experimental results on nine real-world datasets demonstrate that TiReMGE significantly outperforms the nine state-of-the-art baselines.

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