Training Graph Neural Networks on Large Graphs Graph neural networks (GNNs) are a powerful tool for applying machine learning techniques to graph data. They allow us to generate or transform features on vertices and edges using deep neural networks to make them useful for a wide range of applications including fraud detection, product recommendation, and drug discovery. 

To standard traffic analyzers, one click is as good as another. Our impulse purchases and our most prized procurements are all weighted equally by the algorithms through which vendors characterize us as consumers. We still see advertisements for now-shelved quarantine hobbies as we scroll through our daily news because click-counting and similar metrics have failed to reliably predict our actual interest levels.

The financial services sector has many early adopters of sophisticated analytics techniques involving graph computing. Graph AI is regularly used in this industry for applications such as fraud detection, anti-money laundering, and other criminal activities.

As businesses grow and face increasing data challenges, they must find ways to tackle more expansive problems in shorter time windows. The most essential tools a company has at its disposal for addressing real-world data problems are modern algorithms. Businesses that take an algorithmic approach can tackle bigger problems with larger numbers of variables and can make better decisions than those that don't. Data is unquestionably the world’s most valuable resource today.

K-core and k-truss algorithms assist with community search in large graphs and are used to identify cohesive portions of a graph based on specific metrics, particularly in mining applications. Discovering dense subgraphs is an essential task in graph mining, and is valuable in the analysis of social networks, biology, and identifying crime rings. Both algorithms proceed by iteratively removing components that do not meet a specified metric to produce a subgraph. By removing extraneous components, a user of the algorithm can produce simplified versions of large, complex graphs to provide clarity and understandability. Cohesive subgraphs allow the user to identify significant connections within their graph without a great deal of computational requirements.

The clustering coefficient algorithm is typically used on homogeneous, undirected graphs to determine which nodes cluster together and the likelihood that a node’s neighbors are also connected. For example, if a person’s friends are also friends with each other, the node representing that person has a high clustering coefficient. A low clustering coefficient, on the other hand, indicates a graph is composed of several weak ties. A clustering coefficient might be viewed roughly as the ratio of common friends in a social network compared to all possible connections a person might have.

Many networks — of people, bacteria, or computers — have a community-like structure. Quickly finding communities within large networks is a common task of the label propagation algorithm (LPA), a semi-supervised machine learning algorithm that assigns labels to the vertices of a graph that represents such a network.

Louvain clustering is an algorithm for community detection that serves as an unsupervised, agglomerative, bottom-up clustering method for undirected graphs.