Graph partitioning has attracted considerable attention due to its high practicality for real-
world applications. It is particularly relevant to social networks because it enables the …

Large-scale social graph data poses significant challenges for social analytic tools to
monitor and analyze social networks. A feasible solution is to parallelize the computation …

Graph partitioning is a traditional problem with many applications and a number of high-
quality algorithms have been developed. Recently, demand for social network analysis …

Partitioning large graphs is difficult, especially when performed in the limited models of
computation afforded to modern large scale computing systems. In this work we introduce …

Social networks are large graphs that require multiple graph database servers to store and
manage them. Each database server hosts a graph partition with the objectives of balancing …

2 the assembled benchmark suite, the challenges create a reproducible picture of the state
of the art in the area under consideration. This helps to foster an effective technology transfer …

Partitioning graphs at scale is a key challenge for any application that involves distributing a
graph across disks, machines, or data centers. Graph partitioning is a very well studied …

Balanced graph partitioning in the streaming setting is a key problem to enable scalable and
efficient computations on massive graph data such as web graphs, knowledge graphs, and …

With the remarkable proliferation of intelligent mobile devices and fast growing broadband
wireless technology, social networking is undergoing explosive growth in recent years as …

Balanced graph partitioning is an NP-complete problem with a wide range of applications.
These applications include many large-scale distributed problems, including the optimal …