DYMOND: DYnamic MOtif-NoDes Network Generative Model

1 code implementation • 1 Aug 2023 • Giselle Zeno, Timothy La Fond, Jennifer Neville

To address these issues, in this work we propose DYnamic MOtif-NoDes (DYMOND) -- a generative model that considers (i) the dynamic changes in overall graph structure using temporal motif activity and (ii) the roles nodes play in motifs (e. g., one node plays the hub role in a wedge, while the remaining two act as spokes).

Dynamic Vertex Replacement Grammars

1 code implementation • 21 Mar 2023 • Daniel Gonzalez Cedre, Justus Isaiah Hibshman, Timothy La Fond, Grant Boquet, Tim Weninger

Context-free graph grammars have shown a remarkable ability to model structures in real-world relational data.

Graph Similarity

An Ensemble Framework for Detecting Community Changes in Dynamic Networks

no code implementations • 24 Jul 2017 • Timothy La Fond, Geoffrey Sanders, Christine Klymko, Van Emden Henson

In order to represent dynamic networks with evolving communities it is essential to use a dynamic model rather than a static one.

Clustering Stochastic Block Model

Size-Consistent Statistics for Anomaly Detection in Dynamic Networks

no code implementations • 2 Aug 2016 • Timothy La Fond, Jennifer Neville, Brian Gallagher

An important task in network analysis is the detection of anomalous events in a network time series.

Anomaly Detection Time Series +2