Many real-world datasets can be naturally represented as graphs, spanning a wide range of
domains. However, the increasing complexity and size of graph datasets present significant …

As large-scale graphs become more widespread, more and more computational challenges
with extracting, processing, and interpreting large graph data are being exposed. It is …

Graph summarization is the process of computing a compact version of an input graph while
preserving chosen features of its structure. We consider semantic graphs where the features …

Knowledge graph (KG) summarization facilitates efficient information retrieval for exploring
complex structural data. For fast information retrieval, it requires processing on redundant …

Graph Neural Networks (GNNs) are a form of deep learning that enable a wide range of
machine learning applications on graph-structured data. The learning of GNNs, however, is …

Social networking has become a hot topic, in which recommendation algorithms are the
most important. Recently, the combination of deep learning and recommendation algorithms …

Conversational recommender systems use natural language conversations to elicit user
preferences and recommend items proactively. Existing methods based on graph neural …

As large-scale graphs become more widespread, more and more computational challenges
with extracting, processing, and interpreting large graph data are being exposed. It is …

Graphs can represent various phenomena and are increasingly used to tackle complex
problems. Among the challenges associated with graph processing is the ability to analyze …

Scientific datasets are important in scientific research. Researchers always want to find a
way to improve their research. Data discovery and reuse is a recent and popular way of …