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 …

The acquisition, processing, mining, and visualization of sensory data for knowledge
discovery and decision support has recently been a popular area of research and …

Causality analysis generates a dependency graph from system audit logs, which has
emerged as an important solution for attack investigation. In the dependency graph, nodes …

Causal inference is fundamental to empirical scientific discoveries in natural and social
sciences; however, in the process of conducting causal inference, data management …

The increasing scale of encyclopedic knowledge graphs (KGs) calls for summarization as a
way to help users efficiently access and distill world knowledge. Motivated by the disparity …

Graph summarization is beneficial in a wide range of applications, such as visualization,
interactive and exploratory analysis, approximate query processing, reducing the on-disk …

Summarizing graphs is of paramount importance due to diverse applications of large-scale
graph analysis. A popular family of summarization methods is the group-based approach …

Are users of an online social network interested equally in all connections in the network? If
not, how can we obtain a summary of the network personalized to specific users? Can we …

We present new algorithms for graph summarization where the loss in utility is fully
controllable by the user. Specifically, we make three key contributions. First, we present a …

The continuous and rapid growth of highly interconnected datasets, which are both
voluminous and complex, calls for the development of adequate processing and analytical …