25,177 research outputs found
A combined measure for quantifying and qualifying the topology preservation of growing self-organizing maps
The Self-OrganizingMap (SOM) is a neural network model that performs an ordered projection of a high dimensional input space in a low-dimensional topological structure. The process in which such mapping is formed is defined by the SOM algorithm, which is a competitive, unsupervised and nonparametric method, since it does not make any assumption about the input data distribution. The feature maps provided by this algorithm have been successfully applied for vector quantization, clustering and high dimensional data visualization processes. However, the initialization of the network topology and the selection of the SOM training parameters are two difficult tasks caused by the unknown distribution of the input signals. A misconfiguration of these parameters can generate a feature map of low-quality, so it is necessary to have some measure of the degree of adaptation of the SOM network to the input data model. The topologypreservation is the most common concept used to implement this measure. Several qualitative and quantitative methods have been proposed for measuring the degree of SOM topologypreservation, particularly using Kohonen's model. In this work, two methods for measuring the topologypreservation of the Growing Cell Structures (GCSs) model are proposed: the topographic function and the topology preserving ma
Flow-based Influence Graph Visual Summarization
Visually mining a large influence graph is appealing yet challenging. People
are amazed by pictures of newscasting graph on Twitter, engaged by hidden
citation networks in academics, nevertheless often troubled by the unpleasant
readability of the underlying visualization. Existing summarization methods
enhance the graph visualization with blocked views, but have adverse effect on
the latent influence structure. How can we visually summarize a large graph to
maximize influence flows? In particular, how can we illustrate the impact of an
individual node through the summarization? Can we maintain the appealing graph
metaphor while preserving both the overall influence pattern and fine
readability?
To answer these questions, we first formally define the influence graph
summarization problem. Second, we propose an end-to-end framework to solve the
new problem. Our method can not only highlight the flow-based influence
patterns in the visual summarization, but also inherently support rich graph
attributes. Last, we present a theoretic analysis and report our experiment
results. Both evidences demonstrate that our framework can effectively
approximate the proposed influence graph summarization objective while
outperforming previous methods in a typical scenario of visually mining
academic citation networks.Comment: to appear in IEEE International Conference on Data Mining (ICDM),
Shen Zhen, China, December 201
Architectural Considerations for a Self-Configuring Routing Scheme for Spontaneous Networks
Decoupling the permanent identifier of a node from the node's
topology-dependent address is a promising approach toward completely scalable
self-organizing networks. A group of proposals that have adopted such an
approach use the same structure to: address nodes, perform routing, and
implement location service. In this way, the consistency of the routing
protocol relies on the coherent sharing of the addressing space among all nodes
in the network. Such proposals use a logical tree-like structure where routes
in this space correspond to routes in the physical level. The advantage of
tree-like spaces is that it allows for simple address assignment and
management. Nevertheless, it has low route selection flexibility, which results
in low routing performance and poor resilience to failures. In this paper, we
propose to increase the number of paths using incomplete hypercubes. The design
of more complex structures, like multi-dimensional Cartesian spaces, improves
the resilience and routing performance due to the flexibility in route
selection. We present a framework for using hypercubes to implement indirect
routing. This framework allows to give a solution adapted to the dynamics of
the network, providing a proactive and reactive routing protocols, our major
contributions. We show that, contrary to traditional approaches, our proposal
supports more dynamic networks and is more robust to node failures
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