12 citations · 18 across the 6 of their papers we have counts for
6 papers
Edge-based mean-field approximation of dynamics on networks via approximate lumping of Markov chains
Gábor Timár, Jonathan A. Ward, Péter L. Simon
Mean-field approximations for dynamical processes on networks are widely used, but existing derivations often rely either on moment closures or on idealised assumptions about netwo…
Strongly clustered random graphs via triadic closure: Degree correlations and clustering spectrum
Lorenzo Cirigliano, Gareth J. Baxter, Gábor Timár
Real-world networks often exhibit strong transitivity with nontrivial local clustering spectra and degree correlations. Such features are not easily modeled in tractable network mo…
Mean-Field Approximation of Dynamics on Networks
Jonathan A. Ward, Gábor Timár, Péter L. Simon
Many real-world phenomena can be modelled as dynamical processes on networks, a prominent example being the spread of infectious diseases such as COVID-19. Mean-field approximation…
Scaling and universality for percolation in random networks: A unified view
Lorenzo Cirigliano, Gábor Timár, Claudio Castellano
Percolation processes on random networks have been the subject of intense research activity over the last decades: the overall phenomenology of standard percolation on uncorrelated…
Extended-range percolation in complex networks
Lorenzo Cirigliano, Claudio Castellano, Gábor Timár
Classical percolation theory underlies many processes of information transfer along the links of a network. In these standard situations, the requirement for two nodes to be able t…
Localization of nonbacktracking centrality on dense subgraphs of sparse networks
G. Timár, S. N. Dorogovtsev, J. F. F. Mendes
The nonbacktracking matrix, and the related nonbacktracking centrality (NBC) play a crucial role in models of percolation-type processes on networks, such as non-recurrent epidemic…