The persistent challenges of data sparsity and class imbalance have long limited the development of recommender systems. Fortunately, line graph theory offers a novel perspective to overcome these issues. By transforming the user-item interaction bipartite graph into a line graph, the problems of data sparsity and class imbalance are elegantly reformulated as those of insufficient labeled nodes and imbalanced label distribution in the line graph domain. This reformulation allows us to directly apply mature techniques from node classification and imbalanced graph learning to address these core challenges. Inspired by this insight, we propose a Line Graph Data Augmentation (LGDA) strategy, which features two distinct characteristics. Firstly, it is a plug-and-play module that resolves data sparsity and imbalance without modifying the underlying recommendation framework. Secondly, it employs a targeted augmentation and confidence filtering mechanism to generate high-quality, balanced augmented data. Extensive experiments on four real-world datasets validate that LGDA effectively alleviates data sparsity and class imbalance, leading to significant improvements in both recommendation performance and system robustness.

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.