The authors prove that spectral clustering on aggregated event counts consistently recovers communities in a new independent-pair Hawkes block model, and that moment estimators for the Hawkes parameters are consistent.
Models of Continuous-Time Networks with Tie Decay, Diffusion, and Convection
1 Pith paper cite this work. Polarity classification is still indexing.
1
Pith paper citing it
abstract
The study of temporal networks in discrete time has yielded numerous insights into time-dependent networked systems in a wide variety of applications. For many complex systems, however, it is useful to develop continuous-time models of networks and to compare them to associated discrete models. In this paper, we study several continuous-time network models and examine discrete approximations of them both numerically and analytically. To consider continuous-time networks, we associate each edge in a graph with a time-dependent tie strength that can take continuous non-negative values and decays in time after the most recent interaction. We investigate how the mean tie strength evolves with time in several models, and we explore -- both numerically and analytically -- criteria for the emergence of a giant connected component in some of these models. We also briefly examine the effects of interaction patterns of our continuous-time networks on contagion dynamics in a susceptible-infected-recovered model of an infectious disease.
fields
cs.SI 1years
2019 1verdicts
CONDITIONAL 1representative citing papers
citing papers explorer
-
CHIP: A Hawkes Process Model for Continuous-time Networks with Scalable and Consistent Estimation
The authors prove that spectral clustering on aggregated event counts consistently recovers communities in a new independent-pair Hawkes block model, and that moment estimators for the Hawkes parameters are consistent.