Partition of networks into basins of attraction

Shai Carmi*, P. L. Krapivsky, Daniel Ben-Avraham

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

10 Scopus citations


We study partition of networks into basins of attraction based on a steepest ascent search for the node of highest degree. Each node is associated with, or "attracted" to its neighbor of maximal degree, as long as the degree is increasing. A node that has no neighbors of higher degree is a peak, attracting all the nodes in its basin. Maximally random scale-free networks exhibit different behavior based on their degree distribution exponent γ: For small γ (broad distribution) networks are dominated by a giant basin, whereas for large γ (narrow distribution) there are numerous basins, with peaks attracting mainly their nearest neighbors. We derive expressions for the first two moments of the number of basins. We also obtain the complete distribution of basin sizes for a class of hierarchical deterministic scale-free networks that resemble random nets. Finally, we generalize the problem to regular networks and lattices where all degrees are equal, and thus the attractiveness of a node must be determined by an assigned weight, rather than the degree. We derive the complete distribution of basins of attraction resulting from randomly assigned weights in one-dimensional chains.

Original languageAmerican English
Article number066111
JournalPhysical Review E
Issue number6
StatePublished - 1 Dec 2008
Externally publishedYes


Dive into the research topics of 'Partition of networks into basins of attraction'. Together they form a unique fingerprint.

Cite this