While advances in computing resources have made processing enormous amounts of data
possible, human ability to identify patterns in such data has not scaled accordingly. Efficient …

With the impressive growth of available data and the flexibility of network modelling, the
problem of devising effective quantitative methods for the comparison of networks arises …

Comparison of graph structure is a ubiquitous task in data analysis and machine learning,
with diverse applications in fields such as neuroscience, cyber security, social network …

How do we spot interesting events from e-mail or transportation logs? How can we detect
port scan or denial of service attacks from IP-IP communication data? In general, given a …

Sustainable management of natural resources requires adequate scientific knowledge
about complex relationships between human and natural systems. Such understanding is …

As large-scale graphs become more widespread, more and more computational challenges
with extracting, processing, and interpreting large graph data are being exposed. It is …

It is widely accepted that the construction industry is dangerous, and subway construction
projects are more inherently dangerous than general construction projects. On the basis of …

Given a dynamic graph stream, how can we detect the sudden appearance of anomalous
patterns, such as link spam, follower boosting, or denial of service attacks? Additionally, can …

Traditionally most of the anomaly detection algorithms have been designed
for'static'datasets, in which all the observations are available at one time. In non-stationary …

From longitudinal biomedical studies to social networks, graphs have emerged as essential
objects for describing evolving interactions between agents in complex systems. In such …