activity
20142023
most citedThink Locally, Act Locally: The Detection of Small, Medium-Sized, and Large Communities in Large Networks

140 citations · 289 across the 8 of their papers we have counts for

collaborators

9 papers

cs.SI2023★ 1 cited

Raphtory: The temporal graph engine for Rust and Python

Ben Steer, Naomi Arnold, Cheick Tidiane Ba +15

Raphtory is a platform for building and analysing temporal networks. The library includes methods for creating networks from a variety of data sources; algorithms to explore their…

cs.LG2022

Local2Global: A distributed approach for scaling representation learning on graphs

Lucas G. S. Jeub, Giovanni Colavizza, Xiaowen Dong +2

We propose a decentralised "local2global"' approach to graph representation learning, that one can a-priori use to scale any embedding technique. Our local2global approach proceeds…

cs.LG2021★ 2 cited

Local2Global: Scaling global representation learning on graphs via local training

Lucas G. S. Jeub, Giovanni Colavizza, Xiaowen Dong +2

We propose a decentralised "local2global" approach to graph representation learning, that one can a-priori use to scale any embedding technique. Our local2global approach proceeds…

physics.soc-ph2021★ 29 cited

From subcritical behavior to elusive transitions in rumor models

Guilherme Ferraz de Arruda, Lucas G. S. Jeub, Angélica S. Mata +2

Rumor and information spreading are natural processes that emerge from human-to-human interaction. Mathematically, this was explored in the popular Maki-Thompson model, where a pha…

physics.soc-ph2018★ 62 cited

Weight Thresholding on Complex Networks

Xiaoran Yan, Lucas G. S. Jeub, Alessandro Flammini +2

Weight thresholding is a simple technique that aims at reducing the number of edges in weighted networks that are otherwise too dense for the application of standard graph theoreti…

cs.SI2017★ 10 cited

Multiresolution Consensus Clustering in Networks

Lucas G. S. Jeub, Olaf Sporns, Santo Fortunato

Networks often exhibit structure at disparate scales. We propose a method for identifying community structure at different scales based on multiresolution modularity and consensus…

cs.SI2016★ 45 cited

A Framework for the Construction of Generative Models for Mesoscale Structure in Multilayer Networks

Marya Bazzi, Lucas G. S. Jeub, Alex Arenas +2

Multilayer networks allow one to represent diverse and coupled connectivity patterns --- e.g., time-dependence, multiple subsystems, or both --- that arise in many applications and…

cs.SI2015

A Local Perspective on Community Structure in Multilayer Networks

Lucas G. S. Jeub, Michael W. Mahoney, Peter J. Mucha +1

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 networ…

cs.SI2014★ 140 cited

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

Lucas G. S. Jeub, Prakash Balachandran, Mason A. Porter +2

It is common in the study of networks to investigate meso-scale features to try to gain an understanding of network structure and function. For example, numerous algorithms have be…