MAGE, also known as Memgraph Advanced Graph Extensions, is an open-source repository that contains graph algorithms and modules in a form of query modules written by the team behind Memgraph and its users. You can find and contribute implementations of various algorithms in multiple programming languages, all runnable inside Memgraph. This project aims to give everyone the tools they need to tackle the most interesting and challenging graph analytics problems.
You can find the official GitHub repository here: MAGE on GitHub .
Memgraph introduces the concept of query modules, user-defined procedures that extend the Cypher query language. These procedures are grouped into modules that can be loaded into Memgraph. You can find more information on query modules in the official Memgraph documentation.
|distance_calculator||Python||Module for finding the geographical distance between two points defined with 'lng' and 'lat' coordinates.|
|graph_analyzer||Python||This Graph Analyzer query module offers insights about the stored graph or a subgraph.|
|graph_coloring||Python||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.|
|nxalg||Python||A module that provides NetworkX integration with Memgraph and implements many NetworkX algorithms.|
|pagerank||Python||An algorithm for measuring the importance of each node within the graph, based on the number incoming relationships and the importance of the corresponding source nodes.|
|set_cover||Python||The algorithm for finding minimum cost subcollection of sets that covers all elements of a universe.|
|tsp||Python||An algorithm for finding the shortest possible route that visits each vertex exactly once.|
|vrp||Python||Algorithm for finding the shortest route possible between the central depot and places to be visited. The algorithm can be solved with multiple vehicles that represent a visiting fleet.|
|betweenness centrality||C++||The betweenness centrality of a node is defined as the sum of the of all-pairs shortest paths that pass through the node divided by the number of all-pairs shortest paths in the graph. The algorithm has O(nm) time complexity.|
|biconnected_components||C++||Algorithm for calculating maximal biconnected subgraph. A biconnected subgraph is a subgraph with a property that if any vertex were to be removed, the graph will remain connected.|
|bipartite_matching||C++||Algorithm for calculating maximum bipartite matching, where matching is a set of nodes chosen in such a way that no two edges share an endpoint.|
|bridges||C++||A bridge is an edge, which when deleted, increases the number of connected components. Goal of this algorithm is to detect edges which are bridges in graph.|
|cycles||C++||Algorithm for detecting cycles on graphs|
|weakly_connected_components||C++||A module that finds weakly connected components in a graph.|