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Graph Augmented Intelligence and XAI

There are broad-spectrum real-world applications that can be powered by graph technologies. Social networks dwell on graphs that best model how people follow and befriend each other; Biotech and pharmaceutical companies leverage graphs to understand protein interactions and chemical compounds efficacies; Supply chains, telco networks, and power grids are naturally presented as graphs for root-cause analysis; Financial transactions naturally form networks and fraud detections, smart recommendations or asset-liability management are possible to run across these networked data for improved productivity, prediction accuracy or business intelligence. Many industries are looking to graph’s smart (and deep) data processing capabilities to help with their businesses. 

The big-data era started around 2010, as more and more industries are interested in machine learning (and deep learning and AI) to boost their business predictability; some have been using deep learning and specifically varied neural networks to extract more predictive powers. There are three major problems lingering around, though:

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What Should You Know About Graph Database’s Scalability?

Having a distributed and scalable graph database system is highly sought after in many enterprise scenarios. This, on the one hand, is heavily influenced by the sustained rising and popularity of big-data processing frameworks, including but not limited to Hadoop, Spark, and NoSQL databases; on the other hand, as more and more data are to be analyzed in a correlated and multi-dimensional fashion, it's getting difficult to pack all data into one graph on one instance, having a truly distributed and horizontally scalable graph database is a must-have.

Do Not Be Misled

Designing and implementing a scalable graph database system has never been a trivial task. There is a countless number of enterprises, particularly Internet giants, that have explored ways to make graph data processing scalable. Nevertheless, most solutions are either limited to their private and narrow use cases or offer scalability in a vertical fashion with hardware acceleration which only proves, again, that the reason why mainframe architecture computer was deterministically replaced by PC-architecture computer in the 90s was mainly that vertical scalability is generally considered inferior and less-capable-n-scalable than horizontal scalability, period. It has been a norm to perceive that distributed databases use the method of adding cheap PC(s) to achieve scalability (storage and computing) and attempt to store data once and for all on demand. However, doing the same cannot achieve equivalent scalability without massively sacrificing query performance on graph systems.