The performance bottlenecks of graph applications depend not only on the algorithm and
the underlying hardware, but also on the size and structure of the input graph. As a result …

Graph transaction processing poses unique challenges such as random data access due to
the irregularity of graph structures, low throughput and high abort rate due to the relatively …

This paper introduces the recent work of Nebula Graph, an open-source, distributed,
scalable, and native graph database. We present a system design trade-off and a …

Background Climate change significantly impacts health in low-and middle-income
countries (LMICs), exacerbating vulnerabilities. Comprehensive data for evidence-based …

Finding query patterns in a graph is fundamental for graph data analytics. Existing works
mostly focus on either finding a single query pattern or finding patterns in a static graph …

With the growing popularity of various mobile devices, user targeting has received a growing
amount of attention, which aims at effectively and efficiently locating target users that are …

The property graph (PG) model is one of the most general graph data model and has been
widely adopted in many graph analytics and processing systems. However, existing systems …

The performance bottlenecks of graph applications depend not only on the algorithm and
the underlying hardware, but also on the size and structure of the input graph. Programmers …

[目的] 传统的以SQL 为中心的技术栈无法有效地应对大数据场景带来的多元异构数据管理,
大规模关系网络管理和复杂网络分析等挑战, 本文针对新型大数据技术栈展开研究.[方法] …

Graph databases (GDBs) like Neo4j and TigerGraph excel at handling interconnected data
but lack advanced inference capabilities. Neural Graph Databases (NGDBs) address this by …