In the last few years, networks have proven to be a useful tool for modeling structural complexity of a variety of complex systems in different domains including sociology, biology, ethology and computer science. Here, a variety of applications and tools have been recently proposed to ease the analysis and exploration of complex networks. A major trend of work in the area of complex network analysis concerns analyzing simple homogeneous static networks.

However, in real world settings, networks are often dynamic, heterogeneous, and both nodes and links can be described by a set of attributes. Thus, mining attributed  networks is gaining an increasing interest both in the complex network analysis community as well as the web science and data science community with a lot of interesting and promising interactions and emerging of tools and methods usually applied in both communities. On the other hand, coping with link-attributed  networks and more specifically multi-relational networks has also become a hot research topic with a variety of approaches.

One prominent approach is based on the multiplex network  model. A multiplex network is represented as a multi-layer network composed of a set of nodes related to each other with different types of relations. However, these new models, attributed and/or multiplex networks, pose the challenge to provide adequate answers to all basic network analysis tasks that have been studied and provided in the recent few years for the case of simple homogeneous networks. This includes for instance: the problem of node ranking (computing nodes centralities), community detection, link prediction, information diffusion models and network visualization. Almost all work in the field of multiplex network analysis are based on transforming the problem, in a way or another to the classical case of homogeneous network analysis. Existing approaches include: layer aggregation based approaches or ensemble based methods. Little work has focused on analyzing all layers at once. First propositions have also been done to adapt community detection algorithms for attributed networks but there is a need for an in-depth analysis.

This tutorial provides a survey on the latest algorithmic advances for mining attributed networks, covering both node and edge attributed networks.