Graph neural networks (GNNs) are an emerging research field. This specialized deep
neural network architecture is capable of processing graph structured data and bridges the …

We survey foundational features underlying modern graph query languages. We first
discuss two popular graph data models: edge-labelled graphs, where nodes are connected …

Subgraph matching (or subgraph isomorphism) is one of the fundamental problems in graph
analysis. Extensive research has been done to develop practical solutions for subgraph …

Graph databases have gained renewed interest in the last years, due to its applications in
areas such as the Semantic Web and Social Networks Analysis. We study the problem of …

知识图谱是人工智能的重要基石. 各领域大规模知识图谱的构建和发布对知识图谱数据管理提出
了新的挑战. 以数据模型的结构和操作要素为主线, 对目前的知识图谱数据管理理论, 方法 …

Various graphs such as web or social networks may contain up to trillions of edges.
Compressing such datasets can accelerate graph processing by reducing the amount of I/O …

Subgraph query processing (also known as subgraph search) and subgraph matching are
fundamental graph problems in many application domains. A lot of efforts have been made …

Graph databases have received much attention as of late due to numerous applications in
which data is naturally viewed as a graph; these include social networks, RDF and the …

The need to store and manipulate large volume of (unstructured) data has led to the
development of several NoSQL databases for better scalability. Graph databases are a …

Graph data management concerns the research and development of powerful technologies
for storing, processing and analyzing large volumes of graph data. This chapter presents an …