The demand for analyzing patterns and structures of data is growing dramatically in recent
years. The study of network structure is pervasive in sociology, biology, computer science …

Research data sets are growing to unprecedented sizes and network modeling is commonly
used to extract complex relationships in diverse domains, such as genetic interactions …

In the modern world of gigantic datasets, which scientists and practioners of all fields of
learning are confronted with, the availability of robust, scalable and easy-to-use methods for …

Clustering is an important unsupervised classification technique. In supervised
classification, we are provided with a collection of labeled (pre-classified) patterns and the …

Many existing statistical and machine learning tools for social network analysis focus on a
single level of analysis. Methods designed for clustering optimize a global partition of the …

Abstract 'Big'high-dimensional data are commonly analyzed in low-dimensions, after
performing a dimensionality reduction step that inherently distorts the data structure. For a …

Many real-world networks tend to be very dense. Particular examples of interest arise in the
construction of networks that represent pairwise similarities between objects. In these cases …

Social relations are multiplex by nature: actors in a group are tied together by various types
of relationships. To understand and explain group processes it is, therefore, important to …

Many real world systems across multiple disciplines, like social, biological and information
networks can be described as complex networks, ie, assemblies of nodes and edges having …

Networks are a fundamental tool for modeling complex social, technological, and biological
systems. Coupled with the emergence of online social networks and large-scale data …