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On the Estimation of Network Complexity: Dimension of Graphons
Network complexity has been studied for over half a century and has foun...
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Scalable k-NN graph construction
The k-NN graph has played a central role in increasingly popular data-dr...
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The SuperM-Tree: Indexing metric spaces with sized objects
A common approach to implementing similarity search applications is the ...
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Distance-Preserving Graph Embeddings from Random Neural Features
We present Graph Random Neural Features (GRNF), a novel embedding method...
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The Reeb Graph Edit Distance is Universal
We consider the setting of Reeb graphs of piecewise linear functions and...
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Truss-based Structural Diversity Search in Large Graphs
Social decisions made by individuals are easily influenced by informatio...
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Link Dimension and Exact Construction of a Graph
Minimum resolution set and associated metric dimension provide the basis...
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Accurate and Fast Retrieval for Complex Non-metric Data via Neighborhood Graphs
We demonstrate that a graph-based search algorithm-relying on the construction of an approximate neighborhood graph-can directly work with challenging non-metric and/or non-symmetric distances without resorting to metric-space mapping and/or distance symmetrization, which, in turn, lead to substantial performance degradation. Although the straightforward metrization and symmetrization is usually ineffective, we find that constructing an index using a modified, e.g., symmetrized, distance can improve performance. This observation paves a way to a new line of research of designing index-specific graph-construction distance functions.
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