11,267 research outputs found
On the search path length of random binary skip graphs
International audienceIn this paper we consider the skip graph data structure, a load balancing alternative to skip lists, designed to perform better in a distributed environment. We extend previous results of Devroye on skip lists, and prove that the maximum length of a search path in a random binary skip graph of size n is of order log n with high probability.Dans ce travail, nous nous intéressons aux "skip graphs", une alternative aux "skip lists" permettant un meilleur équlibrage de charges et conçue pour être plus efficace dans un environnement distribué. Nous étendons des résultats de Devroye sur les skip lists, et prouvons que la longueur maximale d'un chemin de recherche dans un skip graph binaire aléatoire de taille n, est d'ordre log(n) avec forte probabilité
edge2vec: Representation learning using edge semantics for biomedical knowledge discovery
Representation learning provides new and powerful graph analytical approaches
and tools for the highly valued data science challenge of mining knowledge
graphs. Since previous graph analytical methods have mostly focused on
homogeneous graphs, an important current challenge is extending this
methodology for richly heterogeneous graphs and knowledge domains. The
biomedical sciences are such a domain, reflecting the complexity of biology,
with entities such as genes, proteins, drugs, diseases, and phenotypes, and
relationships such as gene co-expression, biochemical regulation, and
biomolecular inhibition or activation. Therefore, the semantics of edges and
nodes are critical for representation learning and knowledge discovery in real
world biomedical problems. In this paper, we propose the edge2vec model, which
represents graphs considering edge semantics. An edge-type transition matrix is
trained by an Expectation-Maximization approach, and a stochastic gradient
descent model is employed to learn node embedding on a heterogeneous graph via
the trained transition matrix. edge2vec is validated on three biomedical domain
tasks: biomedical entity classification, compound-gene bioactivity prediction,
and biomedical information retrieval. Results show that by considering
edge-types into node embedding learning in heterogeneous graphs,
\textbf{edge2vec}\ significantly outperforms state-of-the-art models on all
three tasks. We propose this method for its added value relative to existing
graph analytical methodology, and in the real world context of biomedical
knowledge discovery applicability.Comment: 10 page
struc2vec: Learning Node Representations from Structural Identity
Structural identity is a concept of symmetry in which network nodes are
identified according to the network structure and their relationship to other
nodes. Structural identity has been studied in theory and practice over the
past decades, but only recently has it been addressed with representational
learning techniques. This work presents struc2vec, a novel and flexible
framework for learning latent representations for the structural identity of
nodes. struc2vec uses a hierarchy to measure node similarity at different
scales, and constructs a multilayer graph to encode structural similarities and
generate structural context for nodes. Numerical experiments indicate that
state-of-the-art techniques for learning node representations fail in capturing
stronger notions of structural identity, while struc2vec exhibits much superior
performance in this task, as it overcomes limitations of prior approaches. As a
consequence, numerical experiments indicate that struc2vec improves performance
on classification tasks that depend more on structural identity.Comment: 10 pages, KDD2017, Research Trac
Learning Generalized Reactive Policies using Deep Neural Networks
We present a new approach to learning for planning, where knowledge acquired
while solving a given set of planning problems is used to plan faster in
related, but new problem instances. We show that a deep neural network can be
used to learn and represent a \emph{generalized reactive policy} (GRP) that
maps a problem instance and a state to an action, and that the learned GRPs
efficiently solve large classes of challenging problem instances. In contrast
to prior efforts in this direction, our approach significantly reduces the
dependence of learning on handcrafted domain knowledge or feature selection.
Instead, the GRP is trained from scratch using a set of successful execution
traces. We show that our approach can also be used to automatically learn a
heuristic function that can be used in directed search algorithms. We evaluate
our approach using an extensive suite of experiments on two challenging
planning problem domains and show that our approach facilitates learning
complex decision making policies and powerful heuristic functions with minimal
human input. Videos of our results are available at goo.gl/Hpy4e3
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