A great variety of systems in nature, society and technology–from the web of sexual contacts
to the Internet, from the nervous system to power grids–can be modeled as graphs of …

The power of any kind of network approach lies in the ability to simplify a complex system so
that one can better understand its function as a whole. Sometimes it is beneficial, however …

Graph data is found in numerous domains such as for the analysis of social networks,
sensor networks, bioinformatics, industrial systems, and chemistry. Analyzing graphs to …

As a consequence of changing economic and social realities, the increased availability of
large-scale, real-world sociographic data has ushered in a new era of research and …

Dynamic networks have recently being recognized as a powerful abstraction to model and
represent the temporal changes and dynamic aspects of the data underlying many complex …

Periodicity is one of the most frequently occurring phenomena for moving objects. Animals
usually have periodic movement behaviors, such as daily foraging behaviors or yearly …

We propose a new method for characterizing the dynamics of complex networks with its
application to the link prediction problem. Our approach is based on the discovery of …

Graph mining has numerous real-world applications. The goal is to extract interesting
subgraphs or patterns in graph databases. Dynamic attributed graphs are more complex …

Graph representations underlie many modern computer applications, capturing the structure
of such diverse networks as the Internet, personal associations, roads, sensors, and …

We focus on the discovery of interesting patterns in dynamic attributed graphs. To this end,
we define the novel problem of mining cohesive co-evolution patterns. Briefly speaking …