Link prediction is an important task that has numerous applications, including in recommender systems and social network analysis. Autoencoder is an effective method for solving the link prediction task. However, most existing autoencoder-based methods neither fully utilize the attribute information of the nodes nor fully take into account the potential data distribution in the graph. In this article, we propose a novel method named attribute-augmented adversarial variational graph autoencoder (A${}^{3}$VGAE), which can effectively solve the above two problems. The method first constructs the attribute structure graph based on the attribute information. Then, it inputs the topological structure graph, the attribute structure graph, and the attribute information into the shared encoder to obtain two latent representations. Besides, the topology structure graph, attribute structure graph, and attribute information are reconstructed by the dual decoder. The adversarial mechanism is introduced to ensure that the two latent representations match specific prior distributions. Extensive experiments conducted on four real-world graph datasets demonstrate the superiority of our proposed A${}^{3}$VGAE in link prediction tasks.

Identifying positive influence dominating set (PIDS) with the smallest cardinality can produce positive effect with the minimal cost on a social network. The purpose of this article is to propose new approximation algorithms for the minimum PIDS problem and its variants such as the minimum connected PIDS and the minimum PIDS of multiplex networks, with the aim of finding target sets with smaller cardinality. Through the design of novel submodular potential function, we theoretically prove that new approximation algorithms yield approximation ratios with same order compared with existing algorithms. We further demonstrate the performance of our algorithm by showcasing its efficacy on several real-world and publicly available instances of social networks, thereby providing additional evidence that our proposed algorithm can identify PIDS with smaller cardinality.

Knowledge graphs (KGs) are structured knowledge bases that represent information as a collection of interconnected entities and relations. Link prediction in KGs aims to infer missing or potential links between entities based on triple facts. Among different link prediction methods, knowledge graph embedding (KGE) has gained widespread popularity, with the goal of learning low-dimensional representations for KGs. However, most present KGE methods struggle to capture both local and global neighborhood information efficiently. Additionally, many hybrid methods have limitations in modeling and capturing interactions between triples. In this article, we propose an entity-relation-guided random walk (ERGRW) method for link prediction in KGs. Unlike conventional approaches that solely focus on entity-based walks, ERGRW creatively introduces relations as objects to walk as well. Inspired by distance-based methods, we design novel random walk rules based on the translation principle within triples. Thus, the ERGRW not only captures local and global neighborhood information but also discovers potential semantic relationships and interactions in the KGs. Furthermore, the encoder–decoder framework of ERGRW is able to learn comprehensive representation and improve link prediction performance. Extensive experiments conducted on four standard datasets demonstrate the superiority of ERGRW for link prediction.

Link prediction is a hot research topic in graph data analytics, which aims to identify the likelihood of a future connection between two nodes in a network. Among the approaches to link prediction, network embedding has gained tremendous popularity in tackling link prediction tasks. Basically, there are several network embedding methods, such as matrix factorization, random walk, and deep learning. However, most of the existing methods for network embedding solely rely on structure information or fail to effectively combine network structure and feature data. To this end, this paper proposes a novel method that learns network embedding with enhanced feature representations for link prediction (EFRLP). Specifically, structure graphs, bipartite graphs, and co-feature graphs are generated from the attributed network, weighted bipartite graphs are then built over bipartite graphs and correlation weights between features. We further adopt a biased random walk to obtain more representative node sequences. With the help of enriched feature representations, not only can the higher-order semantic relationships be effectively captured but also network sparsity can be alleviated. Then, node sequences are selected into the Skip-Gram model for learning low-dimensional embeddings. Finally, the probability of the existence of edges between nodes is predicted by deep fusion. The effectiveness of the EFRLP method is demonstrated by extensive comparison experiments with several baselines on two real-world datasets.

Community detection is one of the hottest research topics in attributed networks analysis. Nonnegative matrix factorization (NMF) is widely used in community detection of attributed networks because of its high interpretability and extensibility. However, the existing NMF based methods still encounter some obstacles which affect the performance of community detection. Firstly, it is impossible to solve the problem of sparse semantic description. Besides, these methods cannot integrate the heterogeneity of topology structure and nodes attributes. Obviously, these methods cannot accurately identify community structure and assign specific semantic descriptions to each community. To overcome the aforementioned problems, we propose a novel method which combines graph neural networks with weighted-traction regularization. Moreover, we use graph neural networks to discover the semantic characteristics between adjacent nodes which can alleviate the problem of sparse semantic description. Furthermore, the regularizer we proposed can improve the performance of community detection in attributed networks. Experiments on some real attributed networks show that the method we proposed not only is better than some representative related methods but also can assign specific semantic descriptions to each community at the same time.