In a Low-Earth Orbit (LEO) satellite network, each satellite covers an area for a limited time interval. Thus, once the serving satellite is about to lose coverage of the area, a handover needs to be performed to let another satellite take over serving the user equipment (UE) in the area. Although many LEO handover algorithms have been proposed in the literature to date, none of them can ensure the minimum handovers of UEs (or the minimum average call blocking rate of satellites) when the network resource is sufficient (or insufficient). In the paper, we fill this gap by proposing two graph-based algorithms for networks with sufficient and insufficient resources, respectively. Simulations show the proposed algorithms have significant improvements in the number of handovers of UEs and the call blocking rate of satellites over the state-of-the-art methods.

The traditional wet-experiment-guided drug discovery is a labor-consuming and time-consuming process. Quite a few computational drug repositioning approaches have been proposed to predict potential drug-disease associations for the discovery of new indications for drugs and new therapies for diseases. Among them, heterogeneous graph neural network-based approaches can learn drug/disease topological representations on heterogeneous graphs and then give precise inferences for unconfirmed drug-disease associations. However, the existing approaches ignored the meta-paths in the drug-disease networks which could enhance the model performance and interpretability. In this study, we first proposed a multi-instance learning-based heterogeneous graph network approach for drug-disease association prediction, which is called MilGNet. Fusing with heterogeneous graph convolutional layer, the MilGNet learns meta-path-level representations for given drug-disease pairs by a novel pseudo meta-path instance generator and a bidirectional translating embedding projector. Then, an attention-based multi-scale interpretable joint predictor is assembled for precise and rational drug-disease association prediction. Comprehensive experiments have demonstrated the effectiveness of MilGNet compared to 6 advanced approaches. Meanwhile, the case study also shows the model interpretability of MilGNet by identifying high confident meta-paths. Our adopted benchmark dataset and source code are available at https://github.com/gu-yaowen/MilGNet..

With software-defined network (SDN) and network function virtualization (NFV) techniques, we can embed the service chain consisting of a sequence of virtualized network functions (VNFs), i.e., we can determine the flow path and deploy the VNFs contained in the service chain at any place on the path. In the literature, the methods of service chain embedding bound the number of VNFs at a node, whereas the link capacities are disregarded and the amount of flows is not considered, which could cause serious congestion. In addition, according to our experiment, the process overhead on a computation node is linear to the total amount of flows processed. In this paper, we propose a method of service chain embedding to maximize the total amount of flows while bounding the process overhead of the flows on a node by its computation capability and the total amount of flows on an link by its bandwidth capacity. To our knowledge, our method is the first approximation algorithm of service chain embedding with considering flow in the literature. Simulations show our algorithm has good performance in terms of the total amount of flows.