399 research outputs found
Properties of stochastic Kronecker graphs
The stochastic Kronecker graph model introduced by Leskovec et al. is a
random graph with vertex set , where two vertices and
are connected with probability
independently of the presence or absence of any other edge, for fixed
parameters . They have shown empirically that the
degree sequence resembles a power law degree distribution. In this paper we
show that the stochastic Kronecker graph a.a.s. does not feature a power law
degree distribution for any parameters . In addition,
we analyze the number of subgraphs present in the stochastic Kronecker graph
and study the typical neighborhood of any given vertex.Comment: 37 pages, 2 figure
The average cut-rank of graphs
The cut-rank of a set of vertices in a graph is defined as the rank
of the matrix over the binary field whose
-entry is if the vertex in is adjacent to the vertex in
and otherwise. We introduce the graph parameter called
the average cut-rank of a graph, defined as the expected value of the cut-rank
of a random set of vertices. We show that this parameter does not increase when
taking vertex-minors of graphs and a class of graphs has bounded average
cut-rank if and only if it has bounded neighborhood diversity. This allows us
to deduce that for each real , the list of induced-subgraph-minimal
graphs having average cut-rank larger than (or at least) is finite. We
further refine this by providing an upper bound on the size of obstruction and
a lower bound on the number of obstructions for average cut-rank at most (or
smaller than) for each real . Finally, we describe
explicitly all graphs of average cut-rank at most and determine up to
all possible values that can be realized as the average cut-rank of some
graph.Comment: 22 pages, 1 figure. The bound is corrected. Accepted to
European J. Combinatoric
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