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 …

Parallel technology boosts data processing in recent years, and parallel direct data
processing on hierarchically compressed documents exhibits great promise. The high …

There has been a growing interest in the graph-streaming setting where a continuous
stream of graph updates is mixed with graph queries. In principle, purely-functional trees are …

We present PANCAKE, the first system to protect key-value stores from access pattern
leakage attacks with small constant factor bandwidth overhead. PANCAKE uses a new …

Various applications model problems as streaming graphs, which need to quickly apply a
stream of updates and run algorithms on the updated graph. Furthermore, many dynamic …

Modern graphs exert colossal time and space pressure on graph analytics applications. In
2022, Facebook social graph reaches 2.91 billion users with trillions of edges. Many …

In this demonstration paper, we present the Graph Based Benchmark Suite (GBBS), a suite
of scalable, provably-efficient implementations of over 20 fundamental graph problems for …

Graph processing has become an important part of various areas of computing, including
machine learning, medical applications, social network analysis, computational sciences …

Streaming graph has been broadly employed across various application domains. It
involves updating edges to the graph and then performing analytics on the updated graph …

Graph processing has become an important part of various areas of computing, including
machine learning, medical applications, social network analysis, computational sciences …