In this paper we introduce LDBC Graphalytics, a new industrial-grade benchmark for graph
analysis platforms. It consists of six deterministic algorithms, standard datasets, synthetic …

There is an increasing interest in executing complex analyses over large graphs, many of
which require processing a large number of multi-hop neighborhoods or subgraphs …

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 …

We demonstrate Gradoop, an open source framework that combines and extends features of
graph database systems with the benefits of distributed graph processing. Using a rich …

With the proliferation of large, irregular, and sparse relational datasets, new storage and
analysis platforms have arisen to fill gaps in performance and capability left by conventional …

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 …

GraphScope is a system and a set of language extensions that enable a new programming
interface for large-scale distributed graph computing. It generalizes previous graph …

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 …

Despite the increasing interest in graph databases their requirements and specifications are
not yet fully understood by everyone, leading to a great deal of variation in the supported …

Analyzing interconnection structures among the data through the use of graph algorithms
and graph analytics has been shown to provide tremendous value in many application …