Large language models (LLMs) have significantly advanced the field of natural language
processing (NLP), providing a highly useful, task-agnostic foundation for a wide range of …

Temporal characteristics are prominently evident in a substantial volume of knowledge,
which underscores the pivotal role of Temporal Knowledge Graphs (TKGs) in both academia …

Knowledge graph reasoning (KGR), aiming to deduce new facts from existing facts based on
mined logic rules underlying knowledge graphs (KGs), has become a fast-growing research …

Abstract Temporal Knowledge Graph Reasoning (TKGR) is the process of utilizing temporal
information to capture complex relations within a Temporal Knowledge Graph (TKG) to infer …

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 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 …

Graph neural networks (GNNs) have exhibited superb power in many graph related tasks.
Existing GNNs can be categorized into spatial GNNs and spectral GNNs. The spatial GNNs …

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

The success of artificial intelligence (AI) in multiple disciplines and vertical domains in recent
years has promoted the evolution of mobile networking and the future Internet toward an AI …

Temporal Knowledge Graph (TKG) reasoning often involves completing missing factual
elements along the timeline. Although existing methods can learn good embeddings for …