49,434 research outputs found
SSBM: A Signed Stochastic Block Model for Multiple Structure Discovery in Large-Scale Exploratory Signed Networks
Signed network structure discovery has received extensive attention and has
become a research focus in the field of network science. However, most of the
existing studies are focused on the networks with a single structure, e.g.,
community or bipartite, while ignoring multiple structures, e.g., the
coexistence of community and bipartite structures. Furthermore, existing
studies were faced with challenge regarding large-scale signed networks due to
their high time complexity, especially when determining the number of clusters
in the observed network without any prior knowledge. In view of this, we
propose a mathematically principled method for signed network multiple
structure discovery named the Signed Stochastic Block Model (SSBM). The SSBM
can capture the multiple structures contained in signed networks, e.g.,
community, bipartite, and coexistence of them, by adopting a probabilistic
model. Moreover, by integrating the minimum message length (MML) criterion and
component-wise EM (CEM) algorithm, a scalable learning algorithm that has the
ability of model selection is proposed to handle large-scale signed networks.
By comparing state-of-the-art methods on synthetic and real-world signed
networks, extensive experimental results demonstrate the effectiveness and
efficiency of SSBM in discovering large-scale exploratory signed networks with
multiple structures
Mobile object location discovery in unpredictable environments
Emerging mobile and ubiquitous computing environments present hard challenges to software engineering. The use of mobile code has been suggested as a natural fit for simplifing software development for these environments. However, the task of discovering mobile code location becomes a problem in unpredictable environments when using existing strategies, designed with fixed and relatively stable networks in mind. This paper introduces AMOS, a mobile code platform augmented with a structured overlay network. We demonstrate how the location discovery strategy of AMOS has better reliability and scalability properties than existing approaches, with minimal communication overhead. Finally, we demonstrate how AMOS can provide autonomous distribution of effort fairly throughout a network using probabilistic methods that requires no global knowledge of host capabilities
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