自对比图扩散网络
Self-Contrastive Graph Diffusion Network
AI总结：
提出自对比图扩散网络SCGDN，通过注意力模块和扩散模块实现无增强的图自对比学习，有效平衡高阶结构信息保留与避免过拟合，性能超越现有对比和经典方法。
摘要：
增强技术和采样策略在对比学习中至关重要，但在大多数现有工作中，增强技术需要精心设计，且其采样策略只能捕获少量内在监督信息。此外，现有方法需要复杂的设计来获得数据的两种不同表示。为克服这些限制，我们提出了一种名为自对比图扩散网络（SCGDN）的新框架。该框架包含两个主要组件：注意力模块（AttM）和扩散模块（DiFM）。AttM聚合高阶结构和特征信息以获得优秀的嵌入，而DiFM通过拉普拉斯扩散学习平衡图中每个节点的状态，并允许邻接和特征信息在图中协同演化。与现有方法不同，SCGDN是一种无增强方法，避免了“采样偏差”和语义漂移，无需预训练。我们基于结构和特征信息对样本进行高质量采样。如果两个节点是邻居，它们被视为彼此的正样本。如果两个断开连接的节点在$k$NN图上也不相关，它们被视为彼此的负样本。对比目标合理地使用了我们提出的采样策略，冗余减少项最小化了嵌入中的冗余信息，并能很好地保留更多判别信息。在这个新框架中，图自对比学习范式展现了强大的力量。SCGDN有效平衡了保留高阶结构信息和避免过拟合。结果表明，SCGDN能够持续地超越对比方法和经典方法。
英文摘要：
Augmentation techniques and sampling strategies are crucial in contrastive learning, but in most existing works, augmentation techniques require careful design, and their sampling strategies can only capture a small amount of intrinsic supervision information. Additionally, the existing methods require complex designs to obtain two different representations of the data. To overcome these limitations, we propose a novel framework called the Self-Contrastive Graph Diffusion Network (SCGDN). Our framework consists of two main components: the Attentional Module (AttM) and the Diffusion Module (DiFM). AttM aggregates higher-order structure and feature information to get an excellent embedding, while DiFM balances the state of each node in the graph through Laplacian diffusion learning and allows the cooperative evolution of adjacency and feature information in the graph. Unlike existing methodologies, SCGDN is an augmentation-free approach that avoids "sampling bias" and semantic drift, without the need for pre-training. We conduct a high-quality sampling of samples based on structure and feature information. If two nodes are neighbors, they are considered positive samples of each other. If two disconnected nodes are also unrelated on $k$NN graph, they are considered negative samples for each other. The contrastive objective reasonably uses our proposed sampling strategies, and the redundancy reduction term minimizes redundant information in the embedding and can well retain more discriminative information. In this novel framework, the graph self-contrastive learning paradigm gives expression to a powerful force. SCGDN effectively balances between preserving high-order structure information and avoiding overfitting. The results manifest that SCGDN can consistently generate outperformance over both the contrastive methods and the classical methods.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
