2,856 research outputs found
LINE: Large-scale Information Network Embedding
This paper studies the problem of embedding very large information networks
into low-dimensional vector spaces, which is useful in many tasks such as
visualization, node classification, and link prediction. Most existing graph
embedding methods do not scale for real world information networks which
usually contain millions of nodes. In this paper, we propose a novel network
embedding method called the "LINE," which is suitable for arbitrary types of
information networks: undirected, directed, and/or weighted. The method
optimizes a carefully designed objective function that preserves both the local
and global network structures. An edge-sampling algorithm is proposed that
addresses the limitation of the classical stochastic gradient descent and
improves both the effectiveness and the efficiency of the inference. Empirical
experiments prove the effectiveness of the LINE on a variety of real-world
information networks, including language networks, social networks, and
citation networks. The algorithm is very efficient, which is able to learn the
embedding of a network with millions of vertices and billions of edges in a few
hours on a typical single machine. The source code of the LINE is available
online.Comment: WWW 201
CSNE: Conditional Signed Network Embedding
Signed networks are mathematical structures that encode positive and negative
relations between entities such as friend/foe or trust/distrust. Recently,
several papers studied the construction of useful low-dimensional
representations (embeddings) of these networks for the prediction of missing
relations or signs. Existing embedding methods for sign prediction generally
enforce different notions of status or balance theories in their optimization
function. These theories, however, are often inaccurate or incomplete, which
negatively impacts method performance.
In this context, we introduce conditional signed network embedding (CSNE).
Our probabilistic approach models structural information about the signs in the
network separately from fine-grained detail. Structural information is
represented in the form of a prior, while the embedding itself is used for
capturing fine-grained information. These components are then integrated in a
rigorous manner. CSNE's accuracy depends on the existence of sufficiently
powerful structural priors for modelling signed networks, currently unavailable
in the literature. Thus, as a second main contribution, which we find to be
highly valuable in its own right, we also introduce a novel approach to
construct priors based on the Maximum Entropy (MaxEnt) principle. These priors
can model the \emph{polarity} of nodes (degree to which their links are
positive) as well as signed \emph{triangle counts} (a measure of the degree
structural balance holds to in a network).
Experiments on a variety of real-world networks confirm that CSNE outperforms
the state-of-the-art on the task of sign prediction. Moreover, the MaxEnt
priors on their own, while less accurate than full CSNE, achieve accuracies
competitive with the state-of-the-art at very limited computational cost, thus
providing an excellent runtime-accuracy trade-off in resource-constrained
situations
CSNE : Conditional Signed Network Embedding
Signed networks are mathematical structures that encode positive and negative relations between entities such as friend/foe or trust/distrust. Recently, several papers studied the construction of useful low-dimensional representations (embeddings) of these networks for the prediction of missing relations or signs. Existing embedding methods for sign prediction generally enforce different notions of status or balance theories in their optimization function. These theories, however, are often inaccurate or incomplete, which negatively impacts method performance.
In this context, we introduce conditional signed network embedding (CSNE). Our probabilistic approach models structural information about the signs in the network separately from fine-grained detail. Structural information is represented in the form of a prior, while the embedding itself is used for capturing fine-grained information. These components are then integrated in a rigorous manner. CSNE's accuracy depends on the existence of sufficiently powerful structural priors for modelling signed networks, currently unavailable in the literature. Thus, as a second main contribution, which we find to be highly valuable in its own right, we also introduce a novel approach to construct priors based on the Maximum Entropy (MaxEnt) principle. These priors can model the polarity of nodes (degree to which their links are positive) as well as signed triangle counts (a measure of the degree structural balance holds to in a network).
Experiments on a variety of real-world networks confirm that CSNE outperforms the state-of-the-art on the task of sign prediction. Moreover, the MaxEnt priors on their own, while less accurate than full CSNE, achieve accuracies competitive with the state-of-the-art at very limited computational cost, thus providing an excellent runtime-accuracy trade-off in resource-constrained situations
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
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