121 citations · 203 across the 6 of their papers we have counts for
6 papers · 1 filter
Flow-Based Local Graph Clustering with Better Seed Set Inclusion
Nate Veldt, Christine Klymko, David Gleich
Flow-based methods for local graph clustering have received significant recent attention for their theoretical cut improvement and runtime guarantees. In this work we present two i…
An Ensemble Framework for Detecting Community Changes in Dynamic Networks
Timothy La Fond, Geoffrey Sanders, Christine Klymko +1
Dynamic networks, especially those representing social networks, undergo constant evolution of their community structure over time. Nodes can migrate between different communities,…
Detecting highly cyclic structure with complex eigenpairs
Christine Klymko, Geoffrey Sanders
Many large, real-world complex networks have rich community structure that a network scientist seeks to understand. These communities may overlap or have intricate internal structu…
Local rewiring algorithms to increase clustering and grow a small world
Jeff Alstott, Christine Klymko, Pamela B. Pyzza +1
Many real-world networks have high clustering among vertices: vertices that share neighbors are often also directly connected to each other. A network's clustering can be a useful…
Using Triangles to Improve Community Detection in Directed Networks
Christine Klymko, David Gleich, Tamara G. Kolda
In a graph, a community may be loosely defined as a group of nodes that are more closely connected to one another than to the rest of the graph. While there are a variety of metric…
Total communicability as a centrality measure
Michele Benzi, Christine Klymko
We examine a node centrality measure based on the notion of total communicability, defined in terms of the row sums of the exponential of the adjacency matrix of the network. We ar…