3.1k citations · 12k across the 15 of their papers we have counts for
16 papers
Efficient community detection of network flows for varying Markov times and bipartite networks
Masoumeh Kheirkhahzadeh, Andrea Lancichinetti, Martin Rosvall
Community detection of network flows conventionally assumes one-step dynamics on the links. For sparse networks and interest in large-scale structures, longer timescales may be mor…
Mapping bilateral information interests using the activity of Wikipedia editors
Fariba Karimi, Ludvig Bohlin, Anna Samoilenko +2
We live in a global village where electronic communication has eliminated the geographical barriers of information exchange. The road is now open to worldwide convergence of inform…
Identifying modular flows on multilayer networks reveals highly overlapping organization in social systems
Manlio De Domenico, Andrea Lancichinetti, Alex Arenas +1
Unveiling the community structure of networks is a powerful methodology to comprehend interconnected systems across the social and natural sciences. To identify different types of…
Robustness of journal rankings by network flows with different amounts of memory
Ludvig Bohlin, Alcides Viamontes Esquivel, Andrea Lancichinetti +1
As the number of scientific journals has multiplied, journal rankings have become increasingly important for scientific decisions. From submissions and subscriptions to grants and…
A high-reproducibility and high-accuracy method for automated topic classification
Andrea Lancichinetti, M. Irmak Sirer, Jane X. Wang +3
Much of human knowledge sits in large databases of unstructured text. Leveraging this knowledge requires algorithms that extract and record metadata on unstructured text documents.…
Memory in network flows and its effects on spreading dynamics and community detection
Martin Rosvall, Alcides V. Esquivel, Andrea Lancichinetti +2
Random walks on networks is the standard tool for modelling spreading processes in social and biological systems. This first-order Markov approach is used in conventional community…