812 research outputs found
Distance, dissimilarity index, and network community structure
We address the question of finding the community structure of a complex
network. In an earlier effort [H. Zhou, {\em Phys. Rev. E} (2003)], the concept
of network random walking is introduced and a distance measure defined. Here we
calculate, based on this distance measure, the dissimilarity index between
nearest-neighboring vertices of a network and design an algorithm to partition
these vertices into communities that are hierarchically organized. Each
community is characterized by an upper and a lower dissimilarity threshold. The
algorithm is applied to several artificial and real-world networks, and
excellent results are obtained. In the case of artificially generated random
modular networks, this method outperforms the algorithm based on the concept of
edge betweenness centrality. For yeast's protein-protein interaction network,
we are able to identify many clusters that have well defined biological
functions.Comment: 10 pages, 7 figures, REVTeX4 forma
Network Topology of an Experimental Futures Exchange
Many systems of different nature exhibit scale free behaviors. Economic
systems with power law distribution in the wealth is one of the examples. To
better understand the working behind the complexity, we undertook an empirical
study measuring the interactions between market participants. A Web server was
setup to administer the exchange of futures contracts whose liquidation prices
were coupled to event outcomes. After free registration, participants started
trading to compete for the money prizes upon maturity of the futures contracts
at the end of the experiment. The evolving `cash' flow network was
reconstructed from the transactions between players. We show that the network
topology is hierarchical, disassortative and scale-free with a power law
exponent of 1.02+-0.09 in the degree distribution. The small-world property
emerged early in the experiment while the number of participants was still
small. We also show power law distributions of the net incomes and
inter-transaction time intervals. Big winners and losers are associated with
high degree, high betweenness centrality, low clustering coefficient and low
degree-correlation. We identify communities in the network as groups of the
like-minded. The distribution of the community sizes is shown to be power-law
distributed with an exponent of 1.19+-0.16.Comment: 6 pages, 12 figure
Infinite-Order Percolation and Giant Fluctuations in a Protein Interaction Network
We investigate a model protein interaction network whose links represent
interactions between individual proteins. This network evolves by the
functional duplication of proteins, supplemented by random link addition to
account for mutations. When link addition is dominant, an infinite-order
percolation transition arises as a function of the addition rate. In the
opposite limit of high duplication rate, the network exhibits giant structural
fluctuations in different realizations. For biologically-relevant growth rates,
the node degree distribution has an algebraic tail with a peculiar rate
dependence for the associated exponent.Comment: 4 pages, 2 figures, 2 column revtex format, to be submitted to PRL 1;
reference added and minor rewording of the first paragraph; Title change and
major reorganization (but no result changes) in response to referee comments;
to be published in PR
Scale free networks from a Hamiltonian dynamics
Contrary to many recent models of growing networks, we present a model with
fixed number of nodes and links, where it is introduced a dynamics favoring the
formation of links between nodes with degree of connectivity as different as
possible. By applying a local rewiring move, the network reaches equilibrium
states assuming broad degree distributions, which have a power law form in an
intermediate range of the parameters used. Interestingly, in the same range we
find non-trivial hierarchical clustering.Comment: 4 pages, revtex4, 5 figures. v2: corrected statements about
equilibriu
- …