Search Results for author: Lucas G. S. Jeub

Found 5 papers, 2 papers with code

Local2Global: A distributed approach for scaling representation learning on graphs

no code implementations12 Jan 2022 Lucas G. S. Jeub, Giovanni Colavizza, Xiaowen Dong, Marya Bazzi, Mihai Cucuringu

Our local2global approach proceeds by first dividing the input graph into overlapping subgraphs (or "patches") and training local representations for each patch independently.

Anomaly Detection Graph Representation Learning

Local2Global: Scaling global representation learning on graphs via local training

2 code implementations26 Jul 2021 Lucas G. S. Jeub, Giovanni Colavizza, Xiaowen Dong, Marya Bazzi, Mihai Cucuringu

Our local2global approach proceeds by first dividing the input graph into overlapping subgraphs (or "patches") and training local representations for each patch independently.

Graph Reconstruction Graph Representation Learning +2

From subcritical behavior to elusive transitions in rumor models

no code implementations23 Feb 2021 Guilherme Ferraz de Arruda, Lucas G. S. Jeub, Angélica S. Mata, Francisco A. Rodrigues, Yamir Moreno

Rumor and information spreading are natural processes that emerge from human-to-human interaction.

Physics and Society

A Local Perspective on Community Structure in Multilayer Networks

no code implementations18 Oct 2015 Lucas G. S. Jeub, Michael W. Mahoney, Peter J. Mucha, Mason A. Porter

The analysis of multilayer networks is among the most active areas of network science, and there are now several methods to detect dense "communities" of nodes in multilayer networks.

Social and Information Networks Probability Adaptation and Self-Organizing Systems Data Analysis, Statistics and Probability Physics and Society

Think Locally, Act Locally: The Detection of Small, Medium-Sized, and Large Communities in Large Networks

1 code implementation15 Mar 2014 Lucas G. S. Jeub, Prakash Balachandran, Mason A. Porter, Peter J. Mucha, Michael W. Mahoney

In this paper, we adopt a complementary perspective that "communities" are associated with bottlenecks of locally-biased dynamical processes that begin at seed sets of nodes, and we employ several different community-identification procedures (using diffusion-based and geodesic-based dynamics) to investigate community quality as a function of community size.

Social and Information Networks Disordered Systems and Neural Networks Combinatorics Adaptation and Self-Organizing Systems Physics and Society

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