Eisha Nathan

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

About

10
Publications
1,932
Reads
How we measure 'reads'
A 'read' is counted each time someone views a publication summary (such as the title, abstract, and list of authors), clicks on a figure, or views or downloads the full-text. Learn more
58
Citations

Publications

Publications (10)
Chapter
Centrality has long been studied as a method of identifying node importance in networks. In this paper we study a variant of several walk-based centrality metrics based on the notion of a nonbacktracking walk, where the pattern is forbidden in the walk. Specifically, we focus our analysis on dynamic graphs, where the underlying data stream the netw...
Conference Paper
Full-text available
Many large datasets from several fields of research such as biology or society can be represented as graphs. Additionally in many real applications, data is constantly being produced, leading to the notion of dynamic graphs. A heavily studied problem is identification of the most important vertices in a graph. This can be done using centrality meas...
Chapter
Full-text available
Dynamic graphs can capture changing relationships in many real datasets that evolve over time. One of the most basic questions about networks is the identification of the “most important” vertices in a network. Measures of vertex importance called centrality measures are used to rank vertices in a graph. In this work, we focus on Katz Centrality. T...
Article
Full-text available
A variety of large datasets, such as social networks or biological data, can be represented as graphs. A common query in graph analysis is to identify the most important vertices in a graph. Centrality metrics are used to obtain numerical scores for each vertex in the graph. The scores are then translated to rankings identifying relative importance...
Article
Full-text available
Many real-world datasets can be represented as graphs. Using iterative solvers to approximate graph centrality measures allows us to obtain a ranking vector on the nodes of the graph, consisting of a number for each vertex in the graph identifying its relative importance. In this work the centrality measures we use are Katz Centrality and PageRank....
Article
Full-text available
Graphs and networks are prevalent in modeling relational datasets from many fields of research. By using iterative solvers to approximate graph measures (specifically Katz Centrality), we can obtain a ranking vector consisting of a number for each vertex in the graph identifying its relative importance. We use the residual to accurately estimate ho...
Article
Full-text available
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...
Conference Paper
Full-text available
Many large datasets from a variety of fields of research can be represented as graphs. A common query is to identify the most important, or highly ranked, vertices in a graph. Centrality metrics are used to obtain numerical scores for each vertex in the graph. The scores can then be translated to rankings identifying relative importance of vertices...

Network

Cited By