68,292 research outputs found
Random Walks on Hypergraphs with Edge-Dependent Vertex Weights
Hypergraphs are used in machine learning to model higher-order relationships
in data. While spectral methods for graphs are well-established, spectral
theory for hypergraphs remains an active area of research. In this paper, we
use random walks to develop a spectral theory for hypergraphs with
edge-dependent vertex weights: hypergraphs where every vertex has a weight
for each incident hyperedge that describes the contribution
of to the hyperedge . We derive a random walk-based hypergraph
Laplacian, and bound the mixing time of random walks on such hypergraphs.
Moreover, we give conditions under which random walks on such hypergraphs are
equivalent to random walks on graphs. As a corollary, we show that current
machine learning methods that rely on Laplacians derived from random walks on
hypergraphs with edge-independent vertex weights do not utilize higher-order
relationships in the data. Finally, we demonstrate the advantages of
hypergraphs with edge-dependent vertex weights on ranking applications using
real-world datasets.Comment: Accepted to ICML 201
Random walks which prefer unvisited edges : exploring high girth even degree expanders in linear time.
Let G = (V,E) be a connected graph with |V | = n vertices. A simple random walk on the vertex set of G is a process, which at each step moves from its current vertex position to a neighbouring vertex chosen uniformly at random. We consider a modified walk which, whenever possible, chooses an unvisited edge for the next transition; and makes a simple random walk otherwise. We call such a walk an edge-process (or E -process). The rule used to choose among unvisited edges at any step has no effect on our analysis. One possible method is to choose an unvisited edge uniformly at random, but we impose no such restriction. For the class of connected even degree graphs of constant maximum degree, we bound the vertex cover time of the E -process in terms of the edge expansion rate of the graph G, as measured by eigenvalue gap 1 -λmax of the transition matrix of a simple random walk on G. A vertex v is ℓ -good, if any even degree subgraph containing all edges incident with v contains at least ℓ vertices. A graph G is ℓ -good, if every vertex has the ℓ -good property. Let G be an even degree ℓ -good expander of bounded maximum degree. Any E -process on G has vertex cover time
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This is to be compared with the Ω(nlog n) lower bound on the cover time of any connected graph by a weighted random walk. Our result is independent of the rule used to select the order of the unvisited edges, which could, for example, be chosen on-line by an adversary. © 2013 Wiley Periodicals, Inc. Random Struct. Alg., 00, 000–000, 2013
As no walk based process can cover an n vertex graph in less than n - 1 steps, the cover time of the E -process is of optimal order when ℓ =Θ (log n). With high probability random r -regular graphs, r ≥ 4 even, have ℓ =Ω (log n). Thus the vertex cover time of the E -process on such graphs is Θ(n)
Hypergraph expanders from Cayley graphs
We present a simple mechanism, which can be randomised, for constructing
sparse -uniform hypergraphs with strong expansion properties. These
hypergraphs are constructed using Cayley graphs over and have
vertex degree which is polylogarithmic in the number of vertices. Their
expansion properties, which are derived from the underlying Cayley graphs,
include analogues of vertex and edge expansion in graphs, rapid mixing of the
random walk on the edges of the skeleton graph, uniform distribution of edges
on large vertex subsets and the geometric overlap property.Comment: 13 page
Local treewidth of random and noisy graphs with applications to stopping contagion in networks
We study the notion of local treewidth in sparse random graphs: the maximum
treewidth over all -vertex subgraphs of an -vertex graph. When is not
too large, we give nearly tight bounds for this local treewidth parameter; we
also derive tight bounds for the local treewidth of noisy trees, trees where
every non-edge is added independently with small probability. We apply our
upper bounds on the local treewidth to obtain fixed parameter tractable
algorithms (on random graphs and noisy trees) for edge-removal problems
centered around containing a contagious process evolving over a network. In
these problems, our main parameter of study is , the number of "infected"
vertices in the network. For a certain range of parameters the running time of
our algorithms on -vertex graphs is , improving
upon the performance of the best-known
algorithms designed for worst-case instances of these edge deletion problems
Local Treewidth of Random and Noisy Graphs with Applications to Stopping Contagion in Networks
We study the notion of local treewidth in sparse random graphs: the maximum treewidth over all k-vertex subgraphs of an n-vertex graph. When k is not too large, we give nearly tight bounds for this local treewidth parameter; we also derive nearly tight bounds for the local treewidth of noisy trees, trees where every non-edge is added independently with small probability. We apply our upper bounds on the local treewidth to obtain fixed parameter tractable algorithms (on random graphs and noisy trees) for edge-removal problems centered around containing a contagious process evolving over a network. In these problems, our main parameter of study is k, the number of initially "infected" vertices in the network. For the random graph models we consider and a certain range of parameters the running time of our algorithms on n-vertex graphs is 2^o(k) poly(n), improving upon the 2^?(k) poly(n) performance of the best-known algorithms designed for worst-case instances of these edge deletion problems
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