Community structure is one of the most relevant features encountered in numerous real-
world applications of networked systems. Despite the tremendous effort of a large …

This chapter characterises the scope of this book. It explains the reasons why one should be
interested in cluster analysis, lists major application areas, basic theoretical and practical …

Existing clustering approaches are usually restricted to crisp clustering, where objects just
belong to one cluster; meanwhile there are some applications where objects could belong to …

In many practical applications of clustering, the objects to be clustered evolve over time, and
a clustering result is desired at each time step. In such applications, evolutionary clustering …

We propose an efficient spectral clustering method for large-scale data. The main idea in our
method consists of employing random Fourier features to explicitly represent data in kernel …

Graph motifs are subgraph patterns that occur in complex networks, which are of key
importance for gaining deep insights into the structure and functionality of the graph. Motif …

With the widespread development of algorithmic fairness, there has been a surge of
research interest that aims to generalize the fairness notions from the attributed data to the …

As an important technique of data analysis, clustering plays an important role in finding the
underlying pattern structure embedded in unlabeled data. Clustering algorithms that need to …

Nowadays, the emergence of new technologies gives rise to a huge amount of data in
different fields such as public transportation, community services, scientific research, etc …

Federated learning (FL) is a distributed machine learning framework that allows resource-
constrained clients to train a global model jointly without compromising data privacy …