Natural graphs with skewed distributions raise unique challenges to distributed graph
computation and partitioning. Existing graph-parallel systems usually use a “one-size-fits-all” …

Given the growing importance of large-scale graph analytics, there is a need to improve the
performance of graph analysis frameworks without compromising on productivity. GraphMat …

There is a growing need to perform a diverse set of real-time analytics (batch and stream
analytics) on evolving graphs to deliver the values of big data to users. The key requirement …

There is great interest in exploiting the opportunity provided by cloud computing platforms
for large-scale analytics. Among these platforms, Apache Spark is growing in popularity for …

Graph algorithms are becoming increasingly important for analyzing large datasets in many
fields. Real-world graph data follows a pattern of sparsity, that is not uniform but highly …

Designing and crafting a static program analysis is challenging due to the complexity of the
task at hand. Among the challenges are modelling the semantics of the input language …

Graph analytic processing has started to become a nearly ubiquitous component in the
enterprise data analytics ecosystem. In response to this growing need, various specialized …

Graph analysis is a powerful method in data analysis. Although several frameworks have
been proposed for processing large graph instances in distributed environments, their …

Due to the growing need to process large graph and network datasets created by modern
applications, recent years have witnessed a surging interest in developing big graph …

Efficient processing of large-scale graphs in distributed environments has been an
increasingly popular topic of research in recent years. Inter-connected data that can be …