155,328 research outputs found
A Latent Parameter Node-Centric Model for Spatial Networks
Spatial networks, in which nodes and edges are embedded in space, play a
vital role in the study of complex systems. For example, many social networks
attach geo-location information to each user, allowing the study of not only
topological interactions between users, but spatial interactions as well. The
defining property of spatial networks is that edge distances are associated
with a cost, which may subtly influence the topology of the network. However,
the cost function over distance is rarely known, thus developing a model of
connections in spatial networks is a difficult task.
In this paper, we introduce a novel model for capturing the interaction
between spatial effects and network structure. Our approach represents a unique
combination of ideas from latent variable statistical models and spatial
network modeling. In contrast to previous work, we view the ability to form
long/short-distance connections to be dependent on the individual nodes
involved. For example, a node's specific surroundings (e.g. network structure
and node density) may make it more likely to form a long distance link than
other nodes with the same degree. To capture this information, we attach a
latent variable to each node which represents a node's spatial reach. These
variables are inferred from the network structure using a Markov Chain Monte
Carlo algorithm.
We experimentally evaluate our proposed model on 4 different types of
real-world spatial networks (e.g. transportation, biological, infrastructure,
and social). We apply our model to the task of link prediction and achieve up
to a 35% improvement over previous approaches in terms of the area under the
ROC curve. Additionally, we show that our model is particularly helpful for
predicting links between nodes with low degrees. In these cases, we see much
larger improvements over previous models
Netter: re-ranking gene network inference predictions using structural network properties
Background: Many algorithms have been developed to infer the topology of gene regulatory networks from gene expression data. These methods typically produce a ranking of links between genes with associated confidence scores, after which a certain threshold is chosen to produce the inferred topology. However, the structural properties of the predicted network do not resemble those typical for a gene regulatory network, as most algorithms only take into account connections found in the data and do not include known graph properties in their inference process. This lowers the prediction accuracy of these methods, limiting their usability in practice.
Results: We propose a post-processing algorithm which is applicable to any confidence ranking of regulatory interactions obtained from a network inference method which can use, inter alia, graphlets and several graph-invariant properties to re-rank the links into a more accurate prediction. To demonstrate the potential of our approach, we re-rank predictions of six different state-of-the-art algorithms using three simple network properties as optimization criteria and show that Netter can improve the predictions made on both artificially generated data as well as the DREAM4 and DREAM5 benchmarks. Additionally, the DREAM5 E. coli. community prediction inferred from real expression data is further improved. Furthermore, Netter compares favorably to other post-processing algorithms and is not restricted to correlation-like predictions. Lastly, we demonstrate that the performance increase is robust for a wide range of parameter settings. Netter is available at http://bioinformatics. intec. ugent. be.
Conclusions: Network inference from high-throughput data is a long-standing challenge. In this work, we present Netter, which can further refine network predictions based on a set of user-defined graph properties. Netter is a flexible system which can be applied in unison with any method producing a ranking from omics data. It can be tailored to specific prior knowledge by expert users but can also be applied in general uses cases. Concluding, we believe that Netter is an interesting second step in the network inference process to further increase the quality of prediction
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
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