网络中的链路预测(Link Prediction)是指如何通过已知的网络节点以及网络结构等信息预测网络中尚未产生连边的两个节点之间产生链接的可能性。这种预测既包含了对未知链接(exist yet unknown links)的预测也包含了对未来链接(future links)的预测。该问题的研究在理论和应用两个方面都具有重要的意义和价值 。

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图神经网络在许多基于图的任务中得到了广泛的应用,如节点分类、链路预测和节点聚类。GNNs的性能优势主要来自于对图的边缘执行特征传播和平滑,因此需要足够的连接性和标签信息来进行有效传播。不幸的是,许多现实世界的网络在边缘和标签方面都是稀疏的,这导致了GNN的次优性能。最近对这个稀疏问题的兴趣集中在自训练方法上,它用伪标签扩展监督信号。然而,由于伪标签的质量和数量都不理想,自训练方法本身并不能充分发挥提炼稀疏图学习性能的潜力。在本文中,我们提出了ROD,一种新的接收感知的在线知识提取方法用于稀疏图学习。我们为ROD设计了三种监督信号:多尺度接收感知的图知识、基于任务的监督和丰富的提炼知识,允许知识以同行教学的方式在线迁移。为了提取隐藏在多尺度接收领域中的知识,ROD明确要求个体学生模型保持不同层次的位置信息。对于给定的任务,每个学生根据自己的接受量表知识进行预测,同时结合多尺度知识动态地建立一个强大的教师。我们的方法已经在9个数据集和各种基于图的任务上进行了广泛的评估,包括节点分类、链接预测和节点聚类。结果表明,ROD算法达到了最先进的性能,对图稀疏性具有更强的鲁棒性。

https://www.zhuanzhi.ai/paper/ff1be0c70de3f486fcb3bc2166e469e9

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Graph neural networks achieve high accuracy in link prediction by jointly leveraging graph topology and node attributes. Topology, however, is represented indirectly; state-of-the-art methods based on subgraph classification label nodes with distance to the target link, so that, although topological information is present, it is tempered by pooling. This makes it challenging to leverage features like loops and motifs associated with network formation mechanisms. We propose a link prediction algorithm based on a new pooling scheme called WalkPool. WalkPool combines the expressivity of topological heuristics with the feature-learning ability of neural networks. It summarizes a putative link by random walk probabilities of adjacent paths. Instead of extracting transition probabilities from the original graph, it computes the transition matrix of a "predictive" latent graph by applying attention to learned features; this may be interpreted as feature-sensitive topology fingerprinting. WalkPool can leverage unsupervised node features or be combined with GNNs and trained end-to-end. It outperforms state-of-the-art methods on all common link prediction benchmarks, both homophilic and heterophilic, with and without node attributes. Applying WalkPool to a set of unsupervised GNNs significantly improves prediction accuracy, suggesting that it may be used as a general-purpose graph pooling scheme.

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Graph neural networks achieve high accuracy in link prediction by jointly leveraging graph topology and node attributes. Topology, however, is represented indirectly; state-of-the-art methods based on subgraph classification label nodes with distance to the target link, so that, although topological information is present, it is tempered by pooling. This makes it challenging to leverage features like loops and motifs associated with network formation mechanisms. We propose a link prediction algorithm based on a new pooling scheme called WalkPool. WalkPool combines the expressivity of topological heuristics with the feature-learning ability of neural networks. It summarizes a putative link by random walk probabilities of adjacent paths. Instead of extracting transition probabilities from the original graph, it computes the transition matrix of a "predictive" latent graph by applying attention to learned features; this may be interpreted as feature-sensitive topology fingerprinting. WalkPool can leverage unsupervised node features or be combined with GNNs and trained end-to-end. It outperforms state-of-the-art methods on all common link prediction benchmarks, both homophilic and heterophilic, with and without node attributes. Applying WalkPool to a set of unsupervised GNNs significantly improves prediction accuracy, suggesting that it may be used as a general-purpose graph pooling scheme.

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