18,397 research outputs found
Signed graph embedding: when everybody can sit closer to friends than enemies
Signed graphs are graphs with signed edges. They are commonly used to
represent positive and negative relationships in social networks. While balance
theory and clusterizable graphs deal with signed graphs to represent social
interactions, recent empirical studies have proved that they fail to reflect
some current practices in real social networks. In this paper we address the
issue of drawing signed graphs and capturing such social interactions. We relax
the previous assumptions to define a drawing as a model in which every vertex
has to be placed closer to its neighbors connected via a positive edge than its
neighbors connected via a negative edge in the resulting space. Based on this
definition, we address the problem of deciding whether a given signed graph has
a drawing in a given -dimensional Euclidean space. We present forbidden
patterns for signed graphs that admit the introduced definition of drawing in
the Euclidean plane and line. We then focus on the -dimensional case, where
we provide a polynomial time algorithm that decides if a given complete signed
graph has a drawing, and constructs it when applicable
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
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