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An Introduction to Graph Data

This article is an excerpt from the book Machine Learning with PyTorch and Scikit-Learn from the best-selling Python Machine Learning series, updated and expanded to cover PyTorch, transformers, and graph neural networks.

Broadly speaking, graphs represent a certain way we describe and capture relationships in data. Graphs are a particular kind of data structure that is nonlinear and abstract. And since graphs are abstract objects, a concrete representation needs to be defined so the graphs can be operated on. Furthermore, graphs can be defined to have certain properties that may require different representations. Figure 1 summarizes the common types of graphs, which we will discuss in more detail in the following subsections:
Common types of graph

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Graph-Based Recommendation System With Milvus

Background

A recommendation system (RS) can identify user preferences based on their historical data and suggest products or items to them accordingly. Companies will enjoy considerable economic benefits from a well-designed recommendation system.

There are three elements in a complete set of recommendation systems: user model, object model, and the core element—recommendation algorithm. Currently, established algorithms include collaborative filtering, implicit semantic modeling, graph-based modeling, combined recommendation, and more. In this article, we will provide some brief instructions on how to use Milvus to build a graph-based recommendation system.

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Positive Impact of Graph Technology and Neural Networks on Cybersecurity

Take a look into the future of cybersecurity

Breaches on the Rise

The Equifax security breach was among the worst ever in terms of the number of people affected and the type of information breached. Information such as names, SSNs, birth dates and addresses are considered the Holy Grail of personal data that allows hackers to gain access to anyone’s personal, financial, and health records.

While frequent incidents of security breaches have brought enough anxiety in corporate America, it’s the complexity of managing cybersecurity and addressing unanswered questions that really have enterprises nervous.