43 citations · 72 across the 20 of their papers we have counts for
7 papers · 1 filter
Graph Clustering in All Parameter Regimes
Junhao Gan, David F. Gleich, Nate Veldt +2
Resolution parameters in graph clustering represent a size and quality trade-off. We address the task of efficiently solving a parameterized graph clustering objective for all valu…
Centrality in dynamic competition networks
Anthony Bonato, Nicole Eikmeier, David F. Gleich +1
Competition networks are formed via adversarial interactions between actors. The Dynamic Competition Hypothesis predicts that influential actors in competition networks should have…
Rigid Graph Alignment
Vikram Ravindra, Huda Nassar, David F. Gleich +1
Graph databases have been the subject of significant research and development. Problems such as modularity, centrality, alignment, and clustering have been formalized and solved in…
Pairwise Link Prediction
Huda Nassar, Austin R. Benson, David F. Gleich
Link prediction is a common problem in network science that transects many disciplines. The goal is to forecast the appearance of new links or to find links missing in the network.…
Triangle Preferential Attachment Has Power-law Degrees and Eigenvalues; Eigenvalues Are More Stable to Network Sampling
Nicole Eikmeier, David F. Gleich
Preferential attachment models are a common class of graph models which have been used to explain why power-law distributions appear in the degree sequences of real network data. O…
Learning Resolution Parameters for Graph Clustering
Nate Veldt, David F. Gleich, Anthony Wirth
Finding clusters of well-connected nodes in a graph is an extensively studied problem in graph-based data analysis. Because of its many applications, a large number of distinct gra…