activity
20112023
most citedAdversarial Graph Augmentation to Improve Graph Contrastive Learning

142 citations · 334 across the 19 of their papers we have counts for

collaborators
Showing cs.SIShow all

5 papers · 1 filter

cs.SI2023★ 29 cited

DYMOND: DYnamic MOtif-NoDes Network Generative Model

Giselle Zeno, Timothy La Fond, Jennifer Neville

Motifs, which have been established as building blocks for network structure, move beyond pair-wise connections to capture longer-range correlations in connections and activity. In…

cs.SI2019

Community detection over a heterogeneous population of non-aligned networks

Guilherme Gomes, Vinayak Rao, Jennifer Neville

Clustering and community detection with multiple graphs have typically focused on aligned graphs, where there is a mapping between nodes across the graphs (e.g., multi-view, multi-…

cs.SI2017★ 1 cited

Identifying User Survival Types via Clustering of Censored Social Network Data

S Chandra Mouli, Abhishek Naik, Bruno Ribeiro +1

The goal of cluster analysis in survival data is to identify clusters that are decidedly associated with the survival outcome. Previous research has explored this problem primarily…

cs.SI2012★ 51 cited

Network Sampling: From Static to Streaming Graphs

Nesreen K. Ahmed, Jennifer Neville, Ramana Kompella

Network sampling is integral to the analysis of social, information, and biological networks. Since many real-world networks are massive in size, continuously evolving, and/or dist…

cs.SI2011

Methods to Determine Node Centrality and Clustering in Graphs with Uncertain Structure

Joseph J. Pfeiffer, Jennifer Neville

Much of the past work in network analysis has focused on analyzing discrete graphs, where binary edges represent the "presence" or "absence" of a relationship. Since traditional ne…