Book Chapter10.1007/978-3-030-88361-4_13
Fast ObjectRank for Large Knowledge Databases.
Hiroaki Shiokawa
- 24 Oct 2021
- pp 217-234
4
TL;DR: SchemaRank as mentioned in this paper dynamically excludes unpromising nodes and edges, ensuring that it detects the same top-k important nodes as ObjectRank, which is an essential tool to evaluate an importance of nodes for a user-specified query in heterogeneous graphs.
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Abstract: ObjectRank is an essential tool to evaluate an importance of nodes for a user-specified query in heterogeneous graphs. However, existing methods are not applicable to massive graphs because they iteratively compute all nodes and edges. This paper proposes SchemaRank, which detects the exact top-k important nodes for a given query within a short running time. SchemaRank dynamically excludes unpromising nodes and edges, ensuring that it detects the same top-k important nodes as ObjectRank. Our extensive evaluations demonstrate that the running time of SchemaRank outperforms existing methods by up to two orders of magnitude.
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Citations
Indexing complex networks for fast attributed kNN queries
TL;DR: Wang et al. as discussed by the authors proposed a novel graph indexing algorithm, namely CT index, for fast k NN queries on large attributed complex networks, which generates two types of indices based on the topological properties of complex networks.
3
Indexing complex networks for fast attributed kNN queries
TL;DR: Wang et al. as discussed by the authors proposed a novel graph indexing algorithm, namely CT index, for fast k NN queries on large attributed complex networks, which generates two types of indices based on the topological properties of complex networks.
Tree-Based Graph Indexing for Fast kNN Queries
TL;DR: Wang et al. as discussed by the authors proposed a novel graph indexing algorithm for fast k-NN queries on complex networks, which generates a tree-based index from a graph so that it avoids to compute redundant paths during kNN queries.
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- 06 Nov 2023
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