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This program utilizes a parallel distributed processing algorithm designed for handling large graphs. It focuses on computing various matching statistics such as node and edge distributions, ...

To address the challenge of processing large-scale graphs, researchers have paid much attention to distributed approaches. The quality of graph partitioning plays a key role in the performance of ...

We study the distributed processing of graph signals that are well represented by graph spectral dictionaries. We first analyze the impact of quantization noise ...

The considerable interest in distributed systems that can execute algorithms to process large graphs has led to the creation of many graph processing systems. However, existing systems suffer from two ...

Learn how to partition, model, and execute graph algorithms in distributed graph databases, and what are the trade-offs and challenges involved.