49,913 research outputs found
Structural matching by discrete relaxation
This paper describes a Bayesian framework for performing relational graph matching by discrete relaxation. Our basic aim is to draw on this framework to provide a comparative evaluation of a number of contrasting approaches to relational matching. Broadly speaking there are two main aspects to this study. Firstly we locus on the issue of how relational inexactness may be quantified. We illustrate that several popular relational distance measures can be recovered as specific limiting cases of the Bayesian consistency measure. The second aspect of our comparison concerns the way in which structural inexactness is controlled. We investigate three different realizations ai the matching process which draw on contrasting control models. The main conclusion of our study is that the active process of graph-editing outperforms the alternatives in terms of its ability to effectively control a large population of contaminating clutter
A Survey on Graph Kernels
Graph kernels have become an established and widely-used technique for
solving classification tasks on graphs. This survey gives a comprehensive
overview of techniques for kernel-based graph classification developed in the
past 15 years. We describe and categorize graph kernels based on properties
inherent to their design, such as the nature of their extracted graph features,
their method of computation and their applicability to problems in practice. In
an extensive experimental evaluation, we study the classification accuracy of a
large suite of graph kernels on established benchmarks as well as new datasets.
We compare the performance of popular kernels with several baseline methods and
study the effect of applying a Gaussian RBF kernel to the metric induced by a
graph kernel. In doing so, we find that simple baselines become competitive
after this transformation on some datasets. Moreover, we study the extent to
which existing graph kernels agree in their predictions (and prediction errors)
and obtain a data-driven categorization of kernels as result. Finally, based on
our experimental results, we derive a practitioner's guide to kernel-based
graph classification
Efficient Algorithms for Node Disjoint Subgraph Homeomorphism Determination
Recently, great efforts have been dedicated to researches on the management
of large scale graph based data such as WWW, social networks, biological
networks. In the study of graph based data management, node disjoint subgraph
homeomorphism relation between graphs is more suitable than (sub)graph
isomorphism in many cases, especially in those cases that node skipping and
node mismatching are allowed. However, no efficient node disjoint subgraph
homeomorphism determination (ndSHD) algorithms have been available. In this
paper, we propose two computationally efficient ndSHD algorithms based on state
spaces searching with backtracking, which employ many heuristics to prune the
search spaces. Experimental results on synthetic data sets show that the
proposed algorithms are efficient, require relative little time in most of the
testing cases, can scale to large or dense graphs, and can accommodate to more
complex fuzzy matching cases.Comment: 15 pages, 11 figures, submitted to DASFAA 200
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