Dynamic & static Katz centrality implementation #117
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jmatak
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type: algorithm
antepusic
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Mar 31, 2022
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Code looks solid! 👍
Quick comment - are the separate empty files in /algorithm_online necessary? The algorithm as implemented has both offline and online functionality.
antepusic
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cpp/mg_utility/mg_utils.hpp
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| std::vector<std::uint64_t> GetEdgeIDsFromList(mgp_list *list) { | ||
| auto size = mgp::list_size(list); | ||
| auto edge_ids = std::vector<std::uint64_t>(size); | ||
| for (std::size_t i = 0; i < size; i++) { | ||
| auto edge = mgp::value_get_edge(mgp::list_at(list, i)); | ||
| auto edge_id = mgp::edge_get_id(edge).as_int; | ||
| edge_ids[i] = edge_id; | ||
| } | ||
| return edge_ids; | ||
| } | ||
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| std::vector<std::uint64_t> GetVerticesFromList(mgp_list *list) { | ||
| auto size = mgp::list_size(list); | ||
| auto vertices = std::vector<std::uint64_t>(size); | ||
| for (std::size_t i = 0; i < size; i++) { | ||
| vertices[i] = mgp::vertex_get_id(mgp::value_get_vertex(mgp::list_at(list, i))).as_int; | ||
| } | ||
| return vertices; | ||
| } |
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I've implemented the GetNodeIDs and GetEdgeEndpointIDs utilities before; these may be redundant.
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This was done before these methods, I should use them now 😄
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My bad. It was here before, just didn't see it. Tnx
antepusic
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Apr 11, 2022
antepusic
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Apr 12, 2022
README.md
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| | [graph_analyzer](python/graph_analyzer.py) | Python | This Graph Analyzer query module offers insights about the stored graph or a subgraph. | | ||
| | [graph_coloring](python/graph_coloring.py) | Python | An algorithm for assigning labels to the graph elements subject to certain constraints. In this form, it is a way of coloring the graph vertices such that no two adjacent vertices are of the same color. | | ||
| | [json_util](python/json_util.py) | Python | A module for loading JSON from a local file or remote address. | ||
| | [katz_centrality](cpp/katz_centrality_module/katz_centrality_module.cpp.py) | C++ | Katz centrality is the centrality measurement that outputs the influence based on the number of shortest paths and their weighted length. |
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| | [katz_centrality](cpp/katz_centrality_module/katz_centrality_module.cpp.py) | C++ | Katz centrality is the centrality measurement that outputs the influence based on the number of shortest paths and their weighted length. | |
| | [katz_centrality](cpp/katz_centrality_module/katz_centrality_module.cpp.py) | C++ | Katz centrality is a centrality measurement that outputs a node's influence based on the number of shortest paths and their weighted length. |
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Description
Implementation of Katz centrality algorithm which calculates the centrality corresponding to the numbers of paths flowing through the node, and their importance based on the walk length. Both static and dynamic algorithms are approximations but have the property that saves the centrality rankings in the final result. The algorithm is based on: Scalable Katz Ranking Computation in Large Static and Dynamic Graphs.
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