MOD

It would be hard to disagree with the contention that graph processing (whether it be of connections between people, shopping habits,...) allows the creation of valuable data-driven products and insights. There is less consensus on the systems that make ...

High performance graph analytics are critical for a long list of application domains. In recent years, the rapid advancement of many-core processors, in particular graphical processing units (GPUs), has sparked a broad interest in developing high ...

Large-scale graph processing systems typically expose a small set of functions, such as the compute() function of Pregel, or the gather(), apply(), and scatter() functions of PowerGraph. For some computations, these APIs are too low-level, yielding long ...

Iterative computations are in the core of large-scale graph processing. In these applications, a set of parameters is continuously refined, until a fixed point is reached. Such fixed point iterations often exhibit non-uniform computational behavior, ...

Subgraph isomorphism, or finding matching patterns in a graph, is a classic graph problem that has many practical use cases. There are even commercialized solutions for this problem such as RDF databases with their support for SPARQL queries. In this ...

Graph data processing is gaining popularity and new solutions are appearing to analyze graphs efficiently. In this paper, we present the prototype for the new query engine of the Sparksee graph database, which is based on an algebra of operations on ...

Social-like graph generators have become an indispensable tool when designing proper evaluation methodologies for social graph applications, algorithms and systems. Existing synthetic generators have been designed to produce data with characteristics ...

This paper presents an empirical study of how a wide spectrum of systems handle the graph pattern matching problem. Our approach is to take the well-known LUBM benchmark, model it across various domains (relational, RDF, property graph), and execute the ...

Native graph query and processing capabilities have become indispensable for modern business applications in enterprise-critical operations on data that is stored in relational database management systems. Traversal operations are a basic ingredient of ...

Graph analytics on big data is currently a very active area of research in both industry and academia. To support graph analytics efficiently a large number of graph processing systems have emerged targeting various perspectives of a graph application ...

We witness an unprecedented proliferation of knowledge graphs that record millions of heterogeneous entities and their diverse relationships. While knowledge graphs are structure-flexible and content-rich, it is difficult to query them. The challenge ...

Finding similar entities over data graphs is an important problem with many applications. Current similarity search algorithms use intuitively appealing heuristics that leverage the link information in the data graph to quantify the degree of similarity ...