Social influence prediction has permeated many domains, including marketing, behavior prediction, recommendation systems, and more. However, traditional methods of predicting social influence not only require domain expertise, they also rely on extracting user features, which can be very tedious. Additionally, graph convolutional networks (GCNs), which deals with graph data in non-Euclidean space, are not directly applicable to Euclidean space. To overcome these problems, we extended DeepInf such that it can predict the social influence of COVID-19 via the transition probability of the page rank domain. Furthermore, our implementation gives rise to a deep learning-based personalized propagation algorithm, called DeepPP. The resulting algorithm combines the personalized propagation of a neural prediction model with the approximate personalized propagation of a neural prediction model from page rank analysis. Four social networks from different domains as well as two COVID-19 datasets were used to analyze the proposed algorithm's efficiency and effectiveness. Compared to other baseline methods, DeepPP provides more accurate social influence predictions. Further, experiments demonstrate that DeepPP can be applied to real-world prediction data for COVID-19.

In conventional federated learning, each device is restricted to train a network model of the same structure. This greatly hinders the application of federated learning where the data and devices are quite heterogeneous because of their different hardware equipment and communication networks. At the same time, existing studies have shown that transmitting all of the model parameters not only has heavy communication costs, but also increases risk of privacy leakage. We propose a general framework for personalized federated learning (PerHeFed), which enables the devices to design their local model structures autonomously and share sub-models without structural restrictions. In PerHeFed, a simple-but-effective mapping relation and a novel personalized sub-model aggregation method are proposed for heterogeneous sub-models to be aggregated. By dividing the aggregations into two primitive types (i.e., inter-layer and intra-layer), PerHeFed is applicable to any combination of heterogeneous convolutional neural networks, and we believe that this can satisfy the personalized requirements of heterogeneous models. Experiments show that, compared to the state-of-the-art method (e.g., FLOP), in non-IID data sets our method compress ≈ 50% of the shared sub-model parameters with only a 4.38% drop in accuracy on SVHN dataset and on CIFAR-10, PerHeFed even achieves a 0.3% improvement in accuracy. To the best of our knowledge, our work is the first general personalized federated learning framework for heterogeneous convolutional networks, even cross different networks, addressing model structure unity in conventional federated learning.

Anonymous peer review is used by the great majority of computer science conferences. OpenReview is such a platform that aims to promote openness in peer review process. The paper, (meta) reviews, rebuttals, and final decisions are all released to public. We collect 11,915 submissions and their 41,276 reviews from the OpenReview platform. We also collect these submissions’ citation data from Google Scholar and their non-peer-reviewed versions from arXiv.org. By acquiring deep insights into these data, we have several interesting findings that could help understand the effectiveness of the public-accessible double-blind peer review process. Our results can potentially help writing a paper, reviewing it, and deciding on its acceptance.

Because of a large number of vehicles in Internet of Vehicle(IoV), distributed nodes and complex driving environment, data security and certification speed are easily affected. Blockchain enables different devices that do not trust each other to work together, maintain the general state in the process of information dissemination and sharing, and protect the privacy of devices. However, at present, the speed of vehicle certification in IoV is slow, and the use of idle resources is not considered. To address this problem, this paper provides a blockchain-based vehicle identity verification scheme by using a hybrid identity code verification method to ensure the nodes in the network securely share information. Meanwhile, a task processing algorithm based on time window is proposed to optimize the utilization of idle resources. In addition, the method is evaluated by simulation experiment, and the designed scheme can reduce malicious behavior of a registered vehicle in the network, and can shorten the processing task delay.

The partial evaluation and assembly framework has recently been applied for processing subgraph matching queries over large-scale knowledge graphs in the distributed environment. The framework is implemented on the master-slave architecture, endowed with outstanding scalability. However, there are two drawbacks of partial evaluation: if the volume of intermediate results is large, a large number of repeated partial matches will be generated; and the assembly computation handled by the master would be a bottleneck. In this paper, we propose an optimal partial evaluation algorithm and a filter method to reduce partial matches by exploring the computing characteristics of partial evaluation and assembly framework. (1) An index structure named inner boundary node index (IBN-Index) is constructed to prune for graph exploration to improve the searching efficiency of the partial evaluation phase. (2) The boundary characteristics of local partial matches are utilized to construct a boundary node index (BN-Index) to reduce the number of local partial matches. (3) The experimental results over benchmark datasets show that our approach outperforms the state-of-the-art methods.

Knowledge graph, as an extension of graph data structure, is being used in a wide range of areas as it can store interrelated data and reveal interlinked relationships between different objects within a large system. This paper proposes an algorithm to construct an access control knowledge graph from user and resource attributes. Furthermore, an online learning framework for access control decision-making is proposed based on the constructed knowledge graph. Within the framework, we extract topological features to represent high cardinality categorical user and resource attributes. Experimental results show that topological features extracted from knowledge graph can improve the access control performance in both offline learning and online learning scenarios with different degrees of class imbalance status.