arXiv subjects

Cheng Zhai

Publications and source records attributed to Cheng Zhai.

3 recordsLinked to original sources

arXiv2026

Knowledge Graphs Meet Graph Neural Networks: A Comprehensive Survey

Graph Neural Networks (GNNs) have emerged as a powerful paradigm in Knowledge Graphs (KGs) due to their intrinsic ability to model graph-structured data. However, there remains a lack of a systematic review about GNN-based methodologies across the entire knowledge graph technologies pipeline. To address this gap, we first propose a novel two-level taxonomy framework for GNN-based knowledge graph technologies: the KG technologies pipeline and GNN-based perspective. Specifically, the knowledge graph technologies pipeline covers knowledge graph construction, knowledge graph embedding, knowledge reasoning and knowledge graph applications. Meanwhile, the GNN-based perspective provides a new categorization of knowledge graph technologies with GNN models, such as GCN, GAT, and HGNN. Then, we analyze the advantages of GNN technology based on the characteristics of different tasks in the knowledge graph lifecycle. Furthermore, we detailed review various GNN-based models for knowledge graph following the proposed taxonomy, and summarize strengths and limitations. Finally, we discuss unresolved challenges and outline promising directions for future research.

cs.LG↗

arXiv2026

A Survey on GNN-based Link Prediction: Techniques, Applications, and Challenges

Graph Neural Networks (GNNs) have emerged as the leading paradigm for link prediction, enabling the inference of missing connections and the anticipation of potential future links. However, existing reviews lack systematic exploration specifically targeting underlying GNN architectures and diverse graph structures. To address this critical gap, this paper provides a comprehensive review of GNN-based link prediction from a novel and dedicated GNN perspective. We propose an innovative taxonomy that categorizes recent advancements based on techniques and applications. From a technique perspective, we focus on key GNN encoder architectures, including GCN-based, GAE-based, GAT-based, and GFormer-based methods, discussing their strengths and limitations. From an application perspective, we highlight prominent use cases of link prediction in knowledge graphs and recommendation systems, demonstrating their real-world impact. In addition, we examine the current challenges and discuss promising future directions.

cs.AI↗

arXiv2021

Enhanced Coalbed Methane Extraction by Geothermal Stimulation in Deep Coal Mines: An Appraisal

Coalbed methane embedded in coal seams, is an unconventional energy resource as well as a hazardous gas existing in mining industries, which attracts lots of global attention. As the largest coal producer, the mining industry in China had to deal with many hazards induced by methane for decades. To solve this issue, underground methane extraction is commonly used in underground coal mines. However, underground methane extraction is hampered by low production rate and low efficiency because of slow gas emission from coal primarily controlled by gas desorption and permeability. It is well known that temperature has a great impact on gas sorption. The higher the temperature the larger the desorption rate. As the depth of coal mines increases beyond 1000m coal mines suffer elevated air temperatures caused by the natural geothermal gradient. The elevated temperature in such mines provides a potential economical way for geothermal energy extraction and utilization in deep coal mines which can largely cut the expenses of installation and operation maintenance. Therefore, a novel method is proposed to enhance underground methane extraction by deep heat stimulation. This paper mainly presents an assessment of previous and ongoing research in the related field and provides a first feasibility analysis of this method applied in the underground environment. The technique proposed in this early appraisal is deemed significant for coalbed methane drainage enhancing the productivity of deep coal mines by geothermal technology and can also be extended for many applications in relevant areas such as shale gas, and tight oil.

physics.geo-ph↗