To avoid the overfitting phenomenon that appeared in performing graph neural networks (GNNs) on test examples, the feature encoding scheme of GNNs usually introduces the dropout procedure. However, after learning latent node representations under this scheme, Gaussian noise produced by the dropout operation is inevitably transmitted into the next neighborhood aggregation step, which necessarily hampers the unbiased aggregation ability of GNN models. To address this issue, in this article, we present a novel aggregator, denoising aggregation (DNAG), which utilizes principal component analysis (PCA) to preserve the aggregated real signals from neighboring features and simultaneously filter out the Gaussian noise. The idea is different from using PCA on traditional applications to reduce the feature dimension. We regard PCA as an aggregator to compress the neighboring node features to have better expressive denoising power. We propose new training architectures to simplify the intensive computation of PCA in DNAG. Numerical experiments show the apparent superiority of the proposed DNAG models in gaining more denoising capability and achieving the state of the art for a set of predictive tasks on several graph-structured datasets.

The key towards learning informative node representations in graphs lies in how to gain contextual information from the neighbourhood. In this work, we present a simple-yet-effective self-supervised node representation learning strategy via directly maximizing the mutual information between the hidden representations of nodes and their neighbourhood, which can be theoretically justified by its link to graph smoothing. Following InfoNCE, our framework is optimized via a surrogate contrastive loss, where the positive selection underpins the quality and efficiency of rep-resentation learning. To this end, we propose a topology-aware positive sampling strategy, which samples positives from the neighbourhood by considering the structural dependencies between nodes and thus enables positive selection upfront. In the extreme case when only one positive is sampled, we fully avoid expensive neighbourhood aggregation. Our methods achieve promising performance on various node classification datasets. It is also worth mentioning by applying our loss function to MLP based node encoders, our methods can be orders of faster than existing solutions. Our codes and supplementary materials are available at https://github.com/dongwei156/n2n.

Abstract Cloud data center consumes enormous amount of electrical power resulting in high operating cost and carbon dioxide emission. Virtual machine (VM) consolidation strives to reallocate VMs to the minimum number of physical machines dynamically to reduce energy consumption. An approach is proposed to solve consolidation problem of VMs toward optimization of their energy consumption. A novel algorithm is presented, and an energy consumption model is also presented to support the algorithm to find optimal solutions. Experimental results show that energy consumption can be reduced greatly by dynamic VM consolidation. In comparison with other classic algorithms, our proposed algorithm outcomes them and achieves average savings of 42% on energy consumption.