In massive and rapid graph streams, a useful and important task is to summarize the
structure of graph streams in order to enable efficient and effective graph query processing …

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 …

Graph coarsening is a dimensionality reduction technique that aims to learn a smaller-
tractable graph while preserving the properties of the original input graph. However, many …

Network science is the field dedicated to the investigation and analysis of complex systems
via their representations as networks. We normally model such networks as graphs: sets of …

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

Tip decomposition has a pivotal role in mining cohesive subgraphs in bipartite graphs by
computing the tip number of each vertex in accordance with the non-trivial motif butterfly ((2 …

Given a graph G and the desired size k in bits, how can we summarize G within k bits, while
minimizing the information loss? Large-scale graphs have become omnipresent, posing …

The rise of streaming big data has revolutionized the domain of real-time analytics, enabling
organizations to process and analyze data as it is generated (Sun et al., 2020). Unlike …

What kind of macroscopic structural and dynamical patterns can we observe in real-world
hypergraphs? What can be underlying local dynamics on individuals, which ultimately lead …