In this paper we describe Pegasus, a big graph mining system built on top of MapReduce, a modern distributed data processing platform. We introduce GIM-V, an ...
In this section, we describe algorithms for mining the structure of big graphs. We first introduce GIM-V, a general prim- itive for big graph mining, and ...
This paper describes Pegasus, a big graph mining system built on top of MapReduce, a modern distributed data processing platform, and introduces GIM-V, ...
In this section, we describe algorithms for mining the structure of big graphs. We first introduce GIM-V, a general prim- itive for big graph mining, and ...
Mining large graphs: Algorithms, inference, and discoveries
ieeexplore.ieee.org › abstract › document
Mining large graphs: Algorithms, inference, and discoveries. Abstract: How do we find patterns and anomalies, on graphs with billions of nodes and edges, which ...
Graph mining is the process of dealing with graph data by using data mining and machine learning techniques to detect useful and unexpected patterns [2] . Big ...
Jul 27, 2023 · Scalable big graph mining using distributed systems creates new opportunities for the discovery of interesting patterns and anomalies on very ...
We start with important graph algorithms that are central to graph mining and pattern discoveries, including graph- based anomaly detection techniques ( ...
Big Graph Mining: Frameworks and Techniques - ScienceDirect.com
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This task consists on using data mining algorithms to discover interesting, unexpected and useful patterns in large amounts of graph data. It aims also to ...
In this work, we address the research challenge of scalability — we show how to run BP on a very large graph with billions of nodes and edges. Our contributions ...
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