Online social networks have gained great success in recent years. Some online social networks only involving users and social links among users can be represented as homogeneous networks. Meanwhile, some other social networks containing abundant information, which include multiple kinds of nodes and complex relationships, can be denoted as heterogeneous networks. Predicting the missing links or...

Graph Neural Networks (GNNs) belong to a class of deep learning methods that are specialized for extracting critical information and making accurate predictions on graph representations. Researchers have been striving adapt neural networks process data over decade. GNNs found practical applications in various fields, including physics simulations, object detection, recommendation systems. Predi...

Multi-relational graph is a ubiquitous and important data structure, allowing flexible representation of multiple types interactions relations between entities. Similar to other graph-structured data, link prediction one the most tasks on multi-relational graphs often used for knowledge completion. When related coexist, it great benefit build larger via integrating smaller ones. The integration...

Link prediction is a fundamental challenge in network science. Among various methods, local similarity indices are widely used for their high cost-performance. However, the performance less robust: some networks highly competitive to state-of-the-art algorithms while other they very poor. Inspired by techniques developed recommender systems, we propose an enhancement framework based on collabor...

Knowledge Graphs (KGs) have found many applications in industrial and academic settings, which turn, motivated considerable research efforts towards large-scale information extraction from a variety of sources. Despite such efforts, it is well known that even the largest KGs suffer incompleteness; Link Prediction (LP) techniques address this issue by identifying missing facts among entities alr...

The applications of knowledge graph have received much attention in the field artificial intelligence. quality graphs is, however, often influenced by missing facts. To predict facts, various solid transformation based models been proposed mapping into low dimensional spaces. However, most existing approaches ignore that there are multiple relations between two entities, which is common real wo...