Large-scale graph analytics has become increasingly common in areas like social networks,
physical sciences, transportation networks, and recommendation systems. Since many such …

Graph analytics, which mainly includes graph processing, graph mining, and graph learning,
has become increasingly important in several domains, including social network analysis …

In many applications of graph processing, the input data is often generated from an
underlying geometric point data set. However, existing high-performance graph processing …

Large-scale graph processing is one of the recently developed significant research areas
relevant to big data analytics. Distributed graph analytics is useful to see the intuitive insights …

Mining a weighted single large graph has recently attracted many researchers. The
WeGraMi algorithm is considered the state-of-the-art among current approaches. It uses a …

As various services have been generating large-scale graphs to represent multiple
relationships between objects, studies have been conducted to obtain subgraphs with …

High-resolution network-based contagion models are being increasingly used to study
complex disease scenarios. Due to network-induced heterogeneity and sophisticated …

Extracting information from growing networks like social networks has a wide domain of
applications. Therefore, many large-scale distributed graph processing systems have been …

This paper presents an innovative prefetching algorithm for a hybrid main memory structure,
which consists of DRAM and phase-change memory. To enhance the efficiency of hybrid …

平衡图划分是改善并行图计算性能的关键. 一个良好的划分算法应保证划分后的子图在负载均衡
的前提下, 减少子图之间的交互边(切割边) 规模, 从而减少网络通信. 对此 …