The digital deep integration of cyber space, physical space and social space facilitates the
formation of Cyber–Physical–Social Systems (CPSS). Knowledge empowers CPSS to be …

Graph Neural Networks (GNNs) have achieved state-of-the-art performance in many high-
impact applications such as fraud detection, information retrieval, and recommender …

Graph Convolutional Network (GCN) is an emerging technique for information retrieval (IR)
applications. While GCN assumes the homophily property of a graph, real-world graphs are …

Abstract Graph Neural Networks (GNNs) conduct message passing which aggregates local
neighbors to update node representations. Such message passing leads to scalability …

Recently, heterogeneous graphs have attracted widespread attention as a powerful and
practical superclass of traditional homogeneous graphs, which reflect the multi-type node …

Legal case retrieval plays an important role in promoting judicial justice and fairness. One of
its greatest challenges is that the definition of relevance goes far beyond the common …

In the big data era, the relationship between entries becomes more and more complex.
Many graph (or network) algorithms have already paid attention to dynamic networks, which …

Conversational search provides a more convenient interface for users to search by allowing
multi-turn interaction with the search engine. However, the effectiveness of the …

Graph neural networks (GNNs) and their variants have generalized deep learning methods
into non-Euclidean graph data, bringing substantial improvement in many graph mining …

Heterogeneous graph neural networks (HGNNs) have emerged as powerful algorithms for
processing heterogeneous graphs (HetGs), widely used in many critical fields. To capture …