Anita Zakrzewska

Georgia Institute of Technology | GT · School of Computational Science & Engineering

Analyzing massive graphs poses challenges due to the vast amount of data available. Extracting smaller relevant subgraphs allows for further visualization and analysis that would otherwise be too computationally intensive. Furthermore, many real data sets are constantly changing, and require algorithms to update as the graph evolves. This work addr...

Many graph datasets originating from online social network, financial or biological sources are too large to store or analyze. The analysis of such networks may be made more tractable if they are reduced to smaller subgraphs via sampling. While most of the known graph sampling methods are designed with static graphs in mind, many real datasets are...

A variety of massive datasets, such as social networks and biological data, are represented as graphs that reveal underlying connections, trends, and anomalies. Community detection is the task of discovering dense groups of vertices in a graph. Its one specific form is seed set expansion, which finds the best local community for a given set of seed...

Community detection, or graph clustering, is the problem of finding dense groups in a graph. This is important for a variety of applications, from social network analysis to biological interactions. While most work in community detection has focused on static graphs, real data is usually dynamic, changing over time. We present a new algorithm for d...

A variety of massive datasets, such as social networks and biological data, are represented as graphs that reveal underlying connections, trends, and anomalies. Community detection is the task of discovering dense groups of vertices in a graph. Its one specific form is seed set expansion, which finds the best local community for a given set of seed...

The increasing energy consumption of high performance computing has resulted in rising operational and environmental costs. Therefore, reducing the energy consumption of computation is an emerging area of interest. We study the approach of data sampling to reduce the energy costs of sparse graph algorithms. The resulting error levels for several gr...

This paper reports on methods and results of an applied research project by a team consisting of SAIC and four universities to develop, integrate, and evaluate new approaches to detect the weak signals characteristic of insider threats on organizations' information systems. Our system combines structural and semantic information from a real corpora...

When threatened by automated attacks, critical systems that require human-controlled responses have difficulty making optimal responses and adapting protections in real-time and may therefore be overwhelmed. Consequently, experts have called for the development of automatic real-time reaction capabilities. However, a technical gap exists in the mod...