Machine Learning in Social Networks: Embedding Nodes, Edges, Communities, and Graphs

Social networks are more than collections of connections: they are structured data that machine-learning methods can represent and analyze. Machine Learning in Social Networks explains how nodes, edges, communities, and whole graphs can be mapped into vector representations—and how those representations can support downstream analysis.

Manasvi Aggarwal and M. N. Murty build the discussion from graph fundamentals through established embedding approaches to deep-learning methods. The result is a concise introduction to network representation learning, with examples of the kinds of analytical tasks these methods are designed to support.

From network structure to useful representations

The book starts with social networks and their representation as graphs, including common ways to store graph data. It then introduces network embeddings and the evaluation of learned representations. This foundation helps readers see how a network’s structure becomes input for machine-learning methods rather than remaining only a picture of connected entities.

Three routes to network embeddings

Several families of methods are covered, including matrix factorization, random-walk approaches, and deep learning. The treatment of node representations includes approaches such as DeepWalk and node2vec, matrix-factorization methods, and graph neural networks. Comparing these approaches offers a way to understand how different techniques capture relationships and structure in a network.

Deep learning in context

A dedicated chapter introduces neural networks and tools including convolutional and recurrent networks and autoencoders. This background connects deep-learning concepts with their role in learning representations, making the methods discussed later easier to place within the wider machine-learning landscape.

More than individual nodes

Network analysis often asks questions about groups and entire structures, not only individual entities. The book extends its focus from node representations to graph embeddings, and connects learned representations with tasks such as classification, clustering, community detection, visualization, link prediction, and network reconstruction.

For students and network researchers

Aimed at senior undergraduate and graduate students and researchers in social and complex networks, this SpringerBrief is suited to readers with some undergraduate-level mathematics. Its compact, six-chapter organization makes it a focused introduction for readers studying how machine learning can represent and analyze connected data.