Simplification of large networks and graphs
AssigneeInternational Business Machines Corporation
InventorsKonstantinos Bekas, Alessandro Curioni
Life of this patent
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Abstract
Embodiments relate to simplifying large and complex networks and graphs using global connectivity information based on calculated node centralities. An aspect includes calculating node centralities of a graph until a designated number of central nodes are detected. A percentage of the central nodes are then selected as pivot nodes. The neighboring nodes to each of the pivot nodes are then collapsed until the graph shrinks to a predefined threshold of total nodes. Responsive to the number of total nodes reaching the predefined threshold, the simplified graph is outputted.
Claims20
A computer-implemented method for reducing a memory footprint of a network, the method comprising:
- utilizing an original network to model dynamics, wherein the original network has an original memory footprint in a computer system;
- calculating node centralities of the original network until a designated number of central nodes are detected, wherein the calculating of the node centralities of the original network further comprises:
- approximating a product of a matrix exponential and a random probe vector of an adjacency matrix, the adjacency matrix representing the original network;
- and
- computing a diagonal of the adjacency matrix based on the product of the matrix exponential and the random probe vector;
- selecting a percentage of the central nodes as pivot nodes;
- collapsing neighboring nodes to each pivot node of the original network to shrink the original network to generate a reduced network until the reduced network reaches a predefined threshold of total nodes;
- and
- utilizing the reduced network to model the dynamics, wherein the reduced network has a reduced memory footprint in the computer system as compared to the original footprint.
A computer system comprising:
- a memory having computer-readable instructions;
- and
- one or more processors for executing the computer-readable instructions, the computer-readable instructions comprising:
- utilizing an original network to model dynamics, wherein the original network has an original memory footprint in a computer system;
- calculating node centralities of the original network until a designated number of central nodes are detected, wherein the calculating of the node centralities of the original network further comprises:
- approximating a product of a matrix exponential and a random probe vector of an adjacency matrix, the adjacency matrix representing the original network;
- and
- computing a diagonal of the adjacency matrix based on the product of the matrix exponential and the random probe vector;
- selecting a percentage of the central nodes as pivot nodes;
- collapsing neighboring nodes to each pivot node of the original network to shrink the original network to generate a reduced network until the reduced network reaches a predefined threshold of total nodes;
- and
- utilizing the reduced network to model the dynamics, wherein the reduced network has a reduced memory footprint in the computer system as compared to the original footprint.
A computer program product for reducing a memory footprint of a network, the computer-program product comprising a computer-readable storage medium having program instructions embodied therewith, the program instructions executable by a processor to cause the processor to perform a method comprising:
- utilizing an original network to model dynamics, wherein the original network has an original memory footprint in a computer system;
- calculating node centralities of the original network until a designated number of central nodes are detected, wherein the calculating of the node centralities of the original network further comprises:
- approximating a product of a matrix exponential and a random probe vector of an adjacency matrix, the adjacency matrix representing the original network;
- and
- computing a diagonal of the adjacency matrix based on the product of the matrix exponential and the random probe vector;
- selecting a percentage of the central nodes as pivot nodes;
- collapsing neighboring nodes to each pivot node of the original network to shrink the original network to generate a reduced network until the reduced network reaches a predefined threshold of total nodes;
- and
- utilizing the reduced network to model the dynamics, wherein the reduced network has a reduced memory footprint in the computer system as compared to the original footprint.
Dependencies are inferred from the claim text.