In this paper, we investigate a realistic but underexplored problem, called few-shot temporal
knowledge graph reasoning, that aims to predict future facts for newly emerging entities …

Network embedding plays a significant role in a variety of applications. To capture the
topology of the network, most of the existing network embedding algorithms follow a …

Finding the proper depth $ d $ of a graph convolutional network (GCN) that provides strong
representation ability has drawn significant attention, yet nonetheless largely remains an …

Network alignment is a critical steppingstone behind a variety of multi-network mining tasks.
Most of the existing methods essentially optimize a Frobenius-like distance or ranking-based …

Finding node correspondence across networks, namely multi-network alignment, is an
essential prerequisite for joint learning on multiple networks. Despite great success in …

Knowledge graph question answering aims to identify answers of the query according to the
facts in the knowledge graph. In the vast majority of the existing works, the input queries are …

Network embedding plays an important role in a variety of social network applications.
Existing network embedding methods, explicitly or implicitly, can be categorized into …

Network alignment plays an important role in a variety of applications. Many traditional
methods explicitly or implicitly assume the alignment consistency which might suffer from …

Graph structures have attracted much research attention for carrying complex relational
information. Based on graphs, many algorithms and tools are proposed and developed for …

A fundamental challenge of bipartite graph representation learning is how to extract
informative node embeddings. Self-Supervised Learning (SSL) is a promising paradigm to …