Real networks exhibit heterogeneous nature with nodes playing far different roles in
structure and function. To identify vital nodes is thus very significant, allowing us to control …

Social data in digital form—including user-generated content, expressed or implicit relations
between people, and behavioral traces—are at the core of popular applications and …

Networks are a fundamental tool for modeling complex systems in a variety of domains
including social and communication networks as well as biology and neuroscience. The …

Driven by the increasing scale and high profile cybersecurity incidents related public data,
recent years we have witnessed a paradigm shift in understanding and defending against …

In the past years, network theory has successfully characterized the interaction among the
constituents of a variety of complex systems, ranging from biological to technological, and …

Tensor algebra is a powerful tool with applications in machine learning, data analytics,
engineering and the physical sciences. Tensors are often sparse and compound operations …

Generalized tensor algebra is a prime candidate for acceleration via customized ASICs.
Modern tensors feature a wide range of data sparsity, with the density of non-zero elements …

The large availability of user provided contents on online social media facilitates people
aggregation around shared beliefs, interests, worldviews and narratives. In spite of the …

Problems involving multiple networks are prevalent in many scientific and other domains. In
particular, network alignment, or the task of identifying corresponding nodes in different …

Abstract Representation learning over graph structured data has been mostly studied in
static graph settings while efforts for modeling dynamic graphs are still scant. In this paper …