63 research outputs found
Random walks on hypergraphs
In the last twenty years network science has proven its strength in modelling
many real-world interacting systems as generic agents, the nodes, connected by
pairwise edges. Yet, in many relevant cases, interactions are not pairwise but
involve larger sets of nodes, at a time. These systems are thus better
described in the framework of hypergraphs, whose hyperedges effectively account
for multi-body interactions. We hereby propose a new class of random walks
defined on such higher-order structures, and grounded on a microscopic physical
model where multi-body proximity is associated to highly probable exchanges
among agents belonging to the same hyperedge. We provide an analytical
characterisation of the process, deriving a general solution for the stationary
distribution of the walkers. The dynamics is ultimately driven by a generalised
random walk Laplace operator that reduces to the standard random walk Laplacian
when all the hyperedges have size 2 and are thus meant to describe pairwise
couplings. We illustrate our results on synthetic models for which we have a
full control of the high-order structures, and real-world networks where
higher-order interactions are at play. As a first application of the method, we
compare the behaviour of random walkers on hypergraphs to that of traditional
random walkers on the corresponding projected networks, drawing interesting
conclusions on node rankings in collaboration networks. As a second
application, we show how information derived from the random walk on
hypergraphs can be successfully used for classification tasks involving objects
with several features, each one represented by a hyperedge. Taken together, our
work contributes to unveiling the effect of higher-order interactions on
diffusive processes in higher-order networks, shading light on mechanisms at
the hearth of biased information spreading in complex networked systems
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
Hyperlink prediction via local random walks and Jensen-Shannon divergence
Many real-world systems involving higher-order interactions can be modeled by
hypergraphs, where vertices represent the systemic units and hyperedges
describe the interactions among them. In this paper, we focus on the problem of
hyperlink prediction which aims at inferring missing hyperlinks based on
observed hyperlinks. We propose three similarity indices for hyperlink
prediction based on local random walks and Jensen-Shannon divergence. Numerical
experiments show that the proposed indices outperform the state-of-the-art
methods on a broad range of datasets.Comment: IoP Latex, 15 pages, 1 figure
On some building blocks of hypergraphs: units, twin-units, regular, co-regular, and symmetric sets
Here, we introduce and investigate different building blocks, named units,
twin units, regular sets, symmetric sets, and co-regular sets in a hypergraph.
Our work shows that the presence of these building blocks leaves certain traces
in the spectrum and the corresponding eigenspaces of the connectivity operators
associated with the hypergraph. We also show that, conversely, some specific
footprints in the spectrum and in the corresponding eigenvectors retrace the
presence of some of these building blocks in the hypergraph. The hypergraph
remains invariant under the permutations among the vertices in some building
blocks. These vertices behave similarly, in random walks on the hypergraph and
play an important role in hypergraph automorphisms. Identifying similar
vertices in certain building blocks results in a smaller hypergraph that
contains some spectral information of the original hypergraph. The number of
specific building blocks provides an upper bound of the chromatic number of the
hypergraph. A pseudo metric is introduced to measure distances between vertices
in the hypergraph by using one of the building blocks. Here, we use the concept
of general connectivity operators of a hypergraph for our spectral study.Comment: Title is changed and some new section adde
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