Global ETD Search
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Showing 1 to 7 of 7 for “"graph kernels"”.
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Weisfeiler-Leman graph kernels for the out-of-distribution characterization of graph structured data
This thesis presents a new metric named Graph Distributional Analytics (GDA). This approach uses Weisfeiler-Leman kernels, cosine similarity, and traditional statistical metrics to better characterize graph-structured data. It focuses on enhancing the analysis of graph-structured data and enhancing …
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Graph kernel extensions and experiments with application to molecule classification, lead hopping and multiple targets
… domains and have been recently adapted for graph structures making them directly applicable to pharmaceutical drug discovery. Specifically graph structures have a natural fit with molecular data, in that a graph consists of a set of nodes that represent atoms that are connected by bonds. In …
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Information overload in structured data
… in two separate structured domains, namely, graphs and text.</p> <p>Graph kernels have been proposed as an efficient and theoretically sound approach to compute graph similarity. They decompose graphs into certain sub-structures, such as subtrees, or subgraphs. However, existing graph kernels …
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List coloring in general graphs
… new approaches to the problem of list-coloring graphs. This is a problem that has its roots in classical graph theory, but has developed an entire theory of its own, that uses tools from structural graph theory, probabilistic approaches, as well as heuristic and algorithmic approaches. This …
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Online and active learning of big networks: theory and algorithms
… active learning approach on a graph, based on generalization error bound minimization. In particular, I present a data-dependent error bound for a graph-based learning method, namely learning with local and global consistency (LLGC). I show that the empirical transductive …
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Identifying protein complexes and disease genes from biomolecular networks
… that protein complexes are densely connected sub-graphs in PPI networks. In this research, a dense sub-graph detection algorithm is first developed following this assumption by using clique seeds and graph entropy. Although the proposed algorithm generates a large number of reasonable predictions …