Graphs naturally arise in many real-world applications, including social analysis [1], fraud detection [2, 3], traffic prediction [4], computer vision [5], and many more. By representing the data as graphs, the structural information can be encoded to model the relations among entities, and furnish more promising insights underlying the data. For example, in a transportation network, nodes are often the sensors and edges represent the spatial proximity among sensors. In addition to the temporal information provided by the sensors themselves, the graph structure modeled by the spatial correlations leads to a prominent improvement in the traffic prediction problem [4]. Moreover, by
Graph data widely exist in many high-impact applications. Inspired by the success of deep learning in grid-structured data, graph neural network models have been proposed to learn powerful node-level or graph-level representation. However, most of the existing graph neural networks suffer from the following limitations: (1) there is limited analysis regarding the graph convolution properties, such as seed-oriented, degree-aware and order-free; (2) the node's degreespecific graph structure is not explicitly expressed in graph convolution for distinguishing structure-aware node neighborhoods;(3) the theoretical explanation regarding the graph-level pooling schemes is unclear.To address these problems, we propose a generic degree-specific graph neural network named DEMO-Net motivated by Weisfeiler-Lehman graph isomorphism test that recursively identifies 1-hop neighborhood structures. In order to explicitly capture the graph topology integrated with node attributes, we argue that graph convolution should have three properties: seed-oriented, degree-aware, order-free. To this end, we propose multi-task graph convolution where each task represents node representation learning for nodes with a specific degree value, thus leading to preserving the degreespecific graph structure. In particular, we design two multi-task learning methods: degree-specific weight and hashing functions for graph convolution. In addition, we propose a novel graph-level pooling/readout scheme for learning graph representation provably lying in a degree-specific Hilbert kernel space. The experimental results on several node and graph classification benchmark data sets demonstrate the effectiveness and efficiency of our proposed DEMO-Net over state-of-the-art graph neural network models.
In this work, we introduce a novel multiple access scheme which is based on the joint design of the system signature matrix at the transmitter and the successive interference cancelation (SIC) based detector at the receiver. The symbols of the different users are judiciously spread in the frequency (space) domain, which can be effectively exploited by the SIC based technique, such as the iterative message-passing algorithm (MPA), to cancel the multi-user interference as well as to obtain diversity gain. Numerical results show that the non-orthogonal system based on the proposed successive interference cancelation amenable multiple access (SAMA) paradigm employing the iterative MPA achieves significant performances gain over the orthogonal one for the same spectral efficiency with affordable complexity.Index Terms-Message-passing algorithm (MPA), successive interference cancelation (SIC), successive interference cancelation amenable multiple access (SAMA).
In this paper, we study capacity bounds for discrete memoryless broadcast channels with confidential messages. Two private messages as well as a common message are transmitted; the common message is to be decoded by both receivers, while each private message is only for its intended receiver. In addition, each private message is to be kept secret from the unintended receiver where secrecy is measured by equivocation. We propose both inner and outer bounds to the rate equivocation region for broadcast channels with confidential messages. The proposed inner bound generalizes Csiszár and Körner's rate equivocation region for broadcast channels with a single confidential message, Liu et al's achievable rate region for broadcast channels with perfect secrecy, Marton's and Gel'fand and Pinsker's achievable rate region for general broadcast channels. Our proposed outer bounds, together with the inner bound, helps establish the rate equivocation region of several classes of discrete memoryless broadcast channels with confidential messages, including less noisy, deterministic, and semi-deterministic channels. Furthermore, specializing to the general broadcast channel by removing the confidentiality constraint, our proposed outer bounds reduce to new capacity outer bounds for the discrete memory broadcast channel.
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