2,406 research outputs found
Efficient Truss Maintenance in Evolving Networks
Truss was proposed to study social network data represented by graphs. A
k-truss of a graph is a cohesive subgraph, in which each edge is contained in
at least k-2 triangles within the subgraph. While truss has been demonstrated
as superior to model the close relationship in social networks and efficient
algorithms for finding trusses have been extensively studied, very little
attention has been paid to truss maintenance. However, most social networks are
evolving networks. It may be infeasible to recompute trusses from scratch from
time to time in order to find the up-to-date -trusses in the evolving
networks. In this paper, we discuss how to maintain trusses in a graph with
dynamic updates. We first discuss a set of properties on maintaining trusses,
then propose algorithms on maintaining trusses on edge deletions and
insertions, finally, we discuss truss index maintenance. We test the proposed
techniques on real datasets. The experiment results show the promise of our
work
Weighted network modules
The inclusion of link weights into the analysis of network properties allows
a deeper insight into the (often overlapping) modular structure of real-world
webs. We introduce a clustering algorithm (CPMw, Clique Percolation Method with
weights) for weighted networks based on the concept of percolating k-cliques
with high enough intensity. The algorithm allows overlaps between the modules.
First, we give detailed analytical and numerical results about the critical
point of weighted k-clique percolation on (weighted) Erdos-Renyi graphs. Then,
for a scientist collaboration web and a stock correlation graph we compute
three-link weight correlations and with the CPMw the weighted modules. After
reshuffling link weights in both networks and computing the same quantities for
the randomised control graphs as well, we show that groups of 3 or more strong
links prefer to cluster together in both original graphs.Comment: 19 pages, 7 figure
Practical and Efficient Split Decomposition via Graph-Labelled Trees
Split decomposition of graphs was introduced by Cunningham (under the name
join decomposition) as a generalization of the modular decomposition. This
paper undertakes an investigation into the algorithmic properties of split
decomposition. We do so in the context of graph-labelled trees (GLTs), a new
combinatorial object designed to simplify its consideration. GLTs are used to
derive an incremental characterization of split decomposition, with a simple
combinatorial description, and to explore its properties with respect to
Lexicographic Breadth-First Search (LBFS). Applying the incremental
characterization to an LBFS ordering results in a split decomposition algorithm
that runs in time , where is the inverse Ackermann
function, whose value is smaller than 4 for any practical graph. Compared to
Dahlhaus' linear-time split decomposition algorithm [Dahlhaus'00], which does
not rely on an incremental construction, our algorithm is just as fast in all
but the asymptotic sense and full implementation details are given in this
paper. Also, our algorithm extends to circle graph recognition, whereas no such
extension is known for Dahlhaus' algorithm. The companion paper [Gioan et al.]
uses our algorithm to derive the first sub-quadratic circle graph recognition
algorithm
Topology of random clique complexes
In a seminal paper, Erdos and Renyi identified the threshold for connectivity
of the random graph G(n,p). In particular, they showed that if p >> log(n)/n
then G(n,p) is almost always connected, and if p << log(n)/n then G(n,p) is
almost always disconnected, as n goes to infinity.
The clique complex X(H) of a graph H is the simplicial complex with all
complete subgraphs of H as its faces. In contrast to the zeroth homology group
of X(H), which measures the number of connected components of H, the higher
dimensional homology groups of X(H) do not correspond to monotone graph
properties. There are nevertheless higher dimensional analogues of the
Erdos-Renyi Theorem.
We study here the higher homology groups of X(G(n,p)). For k > 0 we show the
following. If p = n^alpha, with alpha - 1/(2k+1), then the
kth homology group of X(G(n,p)) is almost always vanishing, and if -1/k < alpha
< -1/(k+1), then it is almost always nonvanishing.
We also give estimates for the expected rank of homology, and exhibit
explicit nontrivial classes in the nonvanishing regime. These estimates suggest
that almost all d-dimensional clique complexes have only one nonvanishing
dimension of homology, and we cannot rule out the possibility that they are
homotopy equivalent to wedges of spheres.Comment: 23 pages; final version, to appear in Discrete Mathematics. At
suggestion of anonymous referee, a section briefly summarizing the
topological prerequisites has been added to make the article accessible to a
wider audienc
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