Source-linked AI summary
Emergence of communities in weighted networks
J. M. Kumpula, J. -P. Onnela, J. Saramaki, K. Kaski, J. Kertesz
TL;DR
The paper asks how dynamically evolving interaction weights contribute to community formation in weighted networks. It introduces a model in which weights guide link formation and strengthening, finding a transition from largely module-free topology to communities as weight reinforcement increases, alongside social-network properties.
Problem
Understanding how communities emerge while accounting for interaction weights remains an important problem because weights can affect network properties and function.
Method
The model couples dynamically generated link weights to topology through weighted local attachment, which favors strong connections, strengthens involved links, and includes random link formation.
Results
Increasing δ produces a transition from small, module-free communities to tighter communities, with δ ≳0.2 yielding average community sizes near 20 and largest communities of several hundred nodes.
Takeaways & Limitations
The results support a mechanism in which structural fluctuations are amplified by repeated weight strengthening, producing communities and social-network properties including weak links.
Abstract
from arXiv · showhide
Topology and weights are closely related in weighted complex networks and this is reflected in their modular structure. We present a simple network model where the weights are generated dynamically and they shape the developing topology. By tuning a model parameter governing the importance of weights, the resulting networks undergo a gradual structural transition from a module free topology to one with communities. The model also reproduces many features of large social networks, including the "weak links" property.