This appendix provides a concise introduction to key machine learning techniques employed throughout the book. It focuses on two main areas: unsupervised learning and Bayesian classification. The appendix begins with an exploration of K-means clustering, a fundamental unsupervised learning algorithm, demonstrating its application to network community detection. It then discusses methods for evaluating unsupervised learning techniques, including confusion matrices and the adjusted Rand index. The silhouette score is introduced as a metric for assessing clustering quality across different numbers of clusters. The appendix concludes with an explanation of the Bayes plugin classifier, a simple yet effective tool for network classification tasks.

This chapter explores advanced applications of network machine learning for multiple networks. We introduce anomaly detection in time series of networks, identifying significant structural changes over time. The chapter then focuses on signal subnetwork estimation for network classification tasks. We present both incoherent and coherent approaches, with incoherent methods identifying edges that best differentiate between network classes, and coherent methods leveraging additional network structure to improve classification accuracy. Practical applications, such as classifying brain networks, are emphasized throughout. These techniques apply to collections of networks, providing a toolkit for analyzing and classifying complex, multinetwork datasets. By integrating previous concepts with new methodologies, we offer a framework for extracting insights and making predictions from diverse network structures with associated attributes.

Under supervised learning, when the output variable is discrete or categorical instead of continuous, one has a classification problem instead of a regression problem. Several classification methods are covered: linear discriminant analysis, logistic regression, naive Bayes classifier, K-nearest neighbours, extreme learning machine classifier and multi-layer perceptron classifier. In classification, the cross-entropy objective function is often used in place of the mean squared error function.