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Knowledge graph representation learning aims to provide accurate entity and relation representations for tasks such as intelligent question answering and recommendation systems. Existing representation learning methods, which only consider triples, are not sufficiently accurate, so some methods use external auxiliary information such as text, type, and time to improve performance. However, they often encode this information independently, which makes it challenging to fully integrate this information with the knowledge graph at a semantic level. In this study, we propose a method called SP-TAG, which realizes the semantic propagation on text-augmented knowledge graphs. Specifically, SP-TAG constructs a text-augmented knowledge graph by extracting named entities from text descriptions and connecting them with the corresponding entities. Then, SP-TAG uses a graph convolutional network to propagate semantic information between the entities and new named entities so that the text and triple structure are fully integrated. The results of experiments on multiple benchmark datasets show that SP-TAG attains competitive performance. When the number of training samples is limited, SP-TAG maintains its high performance, verifying the importance of text augmentation and semantic propagation.

With the increasing proliferation of malicious code, the camouflage of malicious code is more difficult to cope with. Traditional malicious code detection techniques based on byte comparison have limited accuracy. Detection techniques based on traditional machine learning are highly dependent on feature selection, and the quality of the classifier directly affects the detection results; this increases the difficulty of accurately distinguishing the types of malicious code. To address these problems, a deep neural network-based malicious code detection method is proposed in this work. First, the code binary file is transformed into a corresponding gray-scale image, and then the enhanced RGBA image is formed by using an image enhancement scheme based on information entropy and code file structure. Afterwards, a convolutional neural network is used. The network extracts high-dimensional features of the enhanced code image, detects the malicious code, and classifies the malicious code. The experimental results show that the proposed method distinguishes malicious code with 98.83% detection accuracy. Its classification accuracy is 97.74% (with positive samples) and 98.85% (without positive samples). These high levels of accuracy are suitable for current complex and changeable malicious code environments, and can provide a new solution for the current malicious code detection field.