Memgraph 2.21 documentation changes

pages/advanced-algorithms/available-algorithms.md

@@ -24,7 +24,7 @@ library](/advanced-algorithms/install-mage).
 
 | Algorithms                                                                                           | Lang   | Description                                                                                                                                                                                                                       |
 |------------------------------------------------------------------------------------------------------|--------|-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
-| [algo](/advanced-algorithms/available-algorithms/algo)           | C++    | The algo module provides users with a powerful set of graph algorithms, enabling users to perform complex graph-based operations and computations, such as graph traversal, edge detection, and more.  |
+| [algo](/advanced-algorithms/available-algorithms/algo)           | C++    | The algo module provides users with a powerful set of graph algorithms, enabling users to perform complex graph-based operations and computations, such as graph traversal, edge detection, and more.                                                                 |
 | [betweenness_centrality](/advanced-algorithms/available-algorithms/betweenness_centrality)           | C++    | The betweenness centrality of a node is defined as the sum of the of all-pairs shortest paths that run through the node, divided by the number of all-pairs shortest paths in the graph. The algorithm has O(nm) time complexity. |
 | [biconnected_components](/advanced-algorithms/available-algorithms/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](/advanced-algorithms/available-algorithms/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.                                                                                  |
@@ -36,6 +36,7 @@ library](/advanced-algorithms/install-mage).
 | [graph_coloring](/advanced-algorithms/available-algorithms/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.                           |
 | [katz_centrality](/advanced-algorithms/available-algorithms/katz_centrality)                         | C++    | Katz centrality is a centrality measurement that outputs a node's influence based on the number of shortest paths and their weighted length.                                                                                      |
 | [kmeans_clustering](/advanced-algorithms/available-algorithms/kmeans_clustering)                     | Python | An algorithm for clustering given data.                                                                                                                                                                                           |
+| [leiden_community_detection](/advanced-algorithms/available-algorithms/leiden_community_detection)   | C++    | The Leiden method for community detection is an improvement over the Louvain method, designed to find communities with maximum modularity in a graph while addressing issues of disconnected communities. Runs in _O_(L\*E) time and _O_(V\*E) space, where *L* is the number of iterations of the algorithm, E is the number of edges, V is the number of nodes. |
 | [max_flow](/advanced-algorithms/available-algorithms/max_flow)                                       | Python | An algorithm for finding a flow through a graph such that it is the maximum possible flow.                                                                                                                                        |
 | [node_similarity](/advanced-algorithms/available-algorithms/node_similarity)                         | C++    | A module that contains similarity measures for calculating the similarity between two nodes.                                                                                                                                      |
 | [pagerank](/advanced-algorithms/available-algorithms/pagerank)                                       | C++    | Algorithm that yields the influence measurement based on the recursive information about the connected nodes influence.                                                                                                           |

pages/advanced-algorithms/available-algorithms/algo.mdx

@@ -42,7 +42,8 @@ filters.
 
 {<h4> Input: </h4>}
 
-- `subgraph: Graph` (**OPTIONAL**) ➡ A specific subgraph, which is an [object of type Graph](/advanced-algorithms/run-algorithms#run-procedures-on-subgraph) returned by the `project()` function, on which the algorithm is run.
+- `subgraph: Graph` (**OPTIONAL**) ➡ A specific subgraph, which is an [object of type Graph](/advanced-algorithms/run-algorithms#run-procedures-on-subgraph) returned by the `project()` function, on which the algorithm is run. 
+If subgraph is not specified, the algorithm is computed on the entire graph by default.
 - `start_node: Node` ➡ The first node of the returned path.
 - `end_node: Node` ➡ The final node of the returned path.
 - `relationship_types: List[String]` ➡ A list of relationship filters, explained below.
@@ -103,7 +104,9 @@ including any self-referencing relationships.
 
 {<h4> Input: </h4>}
 
-- `subgraph: Graph` (**OPTIONAL**) ➡ A specific subgraph, which is an [object of type Graph](/advanced-algorithms/run-algorithms#run-procedures-on-subgraph) returned by the `project()` function, on which the algorithm is run.
+- `subgraph: Graph` (**OPTIONAL**) ➡ A specific subgraph, which is an [object of type Graph](/advanced-algorithms/run-algorithms#run-procedures-on-subgraph) returned by the `project()` function, on which the algorithm is run. 
+If subgraph is not specified, the algorithm is computed on the entire graph by default.
+
 - `nodes: List[Node]` ➡ A list of all the input nodes.
 
 {<h4> Output: </h4>}
@@ -146,7 +149,8 @@ In case you don't want to use geospatial types, or you want a custom heuristic,
 
 {<h4> Input: </h4>}
 
-- `subgraph: Graph` (**OPTIONAL**) ➡ A specific subgraph, which is an [object of type Graph](/advanced-algorithms/run-algorithms#run-procedures-on-subgraph) returned by the `project()` function, on which the algorithm is run.
+- `subgraph: Graph` (**OPTIONAL**) ➡ A specific subgraph, which is an [object of type Graph](/advanced-algorithms/run-algorithms#run-procedures-on-subgraph) returned by the `project()` function, on which the algorithm is run. 
+If subgraph is not specified, the algorithm is computed on the entire graph by default.
 - `start: Node` ➡ The starting node.
 - `target: Node` ➡ The target node.
 - `config: Map` ➡ The configuration map.

pages/advanced-algorithms/available-algorithms/betweenness_centrality.mdx

@@ -52,7 +52,9 @@ The procedure returns the betweenness centrality values.
 
 {<h4> Input: </h4>} 
 
-- `subgraph: Graph` (**OPTIONAL**) ➡ A specific subgraph, which is an [object of type Graph](/advanced-algorithms/run-algorithms#run-procedures-on-subgraph) returned by the `project()` function, on which the algorithm is run.
+- `subgraph: Graph` (**OPTIONAL**) ➡ A specific subgraph, which is an [object of type Graph](/advanced-algorithms/run-algorithms#run-procedures-on-subgraph) returned by the `project()` function, on which the algorithm is run. 
+If subgraph is not specified, the algorithm is computed on the entire graph by default.
+
 - `directed: boolean (default=True)` ➡ If `False` the direction of the relationships is ignored.
 - `normalized: boolean (default=True)` ➡ If `True` the betweenness values are normalized by
   `2/((n-1)(n-2))` for graphs, and `1/((n-1)(n-2))` for directed graphs where

pages/advanced-algorithms/available-algorithms/biconnected_components.mdx

@@ -46,7 +46,9 @@ The procedure finds biconnected components.
 {<h4> Input: </h4>}
 
 
-- `subgraph: Graph` (**OPTIONAL**) ➡ A specific subgraph, which is an [object of type Graph](/advanced-algorithms/run-algorithms#run-procedures-on-subgraph) returned by the `project()` function, on which the algorithm is run.
+- `subgraph: Graph` (**OPTIONAL**) ➡ A specific subgraph, which is an [object of type Graph](/advanced-algorithms/run-algorithms#run-procedures-on-subgraph) returned by the `project()` function, on which the algorithm is run. 
+If subgraph is not specified, the algorithm is computed on the entire graph by default.
+
 
 
 {<h4> Output: </h4>}

pages/advanced-algorithms/available-algorithms/bipartite_matching.mdx

@@ -48,7 +48,9 @@ relationship connects nodes between these sets.
 
 {<h4> Input: </h4>}
 
-- `subgraph: Graph` (**OPTIONAL**) ➡ A specific subgraph, which is an [object of type Graph](/advanced-algorithms/run-algorithms#run-procedures-on-subgraph) returned by the `project()` function, on which the algorithm is run.
+- `subgraph: Graph` (**OPTIONAL**) ➡ A specific subgraph, which is an [object of type Graph](/advanced-algorithms/run-algorithms#run-procedures-on-subgraph) returned by the `project()` function, on which the algorithm is run. 
+If subgraph is not specified, the algorithm is computed on the entire graph by default.
+
 
 {<h4> Output: </h4>}
 

pages/advanced-algorithms/available-algorithms/bridges.mdx

@@ -45,7 +45,9 @@ creates two disjoint graph components.
 
 {<h4> Input: </h4>}
 
-- `subgraph: Graph` (**OPTIONAL**) ➡ A specific subgraph, which is an [object of type Graph](/advanced-algorithms/run-algorithms#run-procedures-on-subgraph) returned by the `project()` function, on which the algorithm is run.
+- `subgraph: Graph` (**OPTIONAL**) ➡ A specific subgraph, which is an [object of type Graph](/advanced-algorithms/run-algorithms#run-procedures-on-subgraph) returned by the `project()` function, on which the algorithm is run. 
+If subgraph is not specified, the algorithm is computed on the entire graph by default.
+
 
 {<h4> Output: </h4>}
 

pages/advanced-algorithms/available-algorithms/community_detection.mdx

@@ -56,10 +56,12 @@ Computes graph communities using the Louvain method.
 
 {<h4> Input: </h4>}
 
-- `subgraph: Graph` (**OPTIONAL**) ➡ A specific subgraph, which is an [object of type Graph](/advanced-algorithms/run-algorithms#run-procedures-on-subgraph) returned by the `project()` function, on which the algorithm is run.
-- `weight: string (default=null)` ➡ Specifies the default relationship weight. If not set, 
-  the algorithm uses the `weight` relationship attribute when present and otherwise 
-  treats the graph as unweighted.
+- `subgraph: Graph` (**OPTIONAL**) ➡ A specific subgraph, which is an [object of type Graph](/advanced-algorithms/run-algorithms#run-procedures-on-subgraph) returned by the `project()` function, on which the algorithm is run. 
+If subgraph is not specified, the algorithm is computed on the entire graph by default.
+
+- `weight: string (default=null)` ➡ Specifies the name of the property containing the edge weight. Users can set their own weight property; 
+if this property is not specified, the algorithm uses the `weight` edge attribute by default. If neither is set, each edge's weight defaults to `1`. 
+To utilize a custom weight property, the user must set the `--storage-properties-on-edges=true` flag.
 - `coloring: boolean (default=False)` ➡ If set, use the graph coloring heuristic for effective parallelization.
 - `min_graph_shrink: integer (default=100000)` ➡ The graph coarsening optimization stops upon shrinking the graph to this many nodes.
 - `community_alg_threshold: double (default=0.000001)` ➡ Controls how long the algorithm iterates. When the gain in modularity
@@ -72,7 +74,7 @@ Computes graph communities using the Louvain method.
 
 {<h4> Output: </h4>}
 
-- `node: Vertex` ➡ Graph node.
+- `node: Vertex` ➡ A graph node for which the algorithm was performed and returned as part of the results.
 - `community_id: integer` ➡ Community ID. Defaults to $-1$ if the node does not belong to any community.
 
 {<h4> Usage: </h4>}
@@ -90,12 +92,14 @@ Computes graph communities over a subgraph using the Louvain method.
 
 {<h4> Input: </h4>}
 
-- `subgraph: Graph` (**OPTIONAL**) ➡ A specific subgraph, which is an [object of type Graph](/advanced-algorithms/run-algorithms#run-procedures-on-subgraph) returned by the `project()` function, on which the algorithm is run.
+- `subgraph: Graph` (**OPTIONAL**) ➡ A specific subgraph, which is an [object of type Graph](/advanced-algorithms/run-algorithms#run-procedures-on-subgraph) returned by the `project()` function, on which the algorithm is run. 
+If subgraph is not specified, the algorithm is computed on the entire graph by default.
+
 - `subgraph_nodes: List[Node]` ➡ List of nodes in the subgraph.
 - `subgraph_relationships: List[Relationship]` ➡ List of relationships in the subgraph.
-- `weight: str (default=null)` ➡ Specifies the default relationship weight. If not set,
-  the algorithm uses the `weight` relationship attribute when present and otherwise
-  treats the graph as unweighted.
+- `weight: string (default=null)` ➡ Specifies the name of the property containing the edge weight. Users can set their own weight property; 
+if this property is not specified, the algorithm uses the `weight` edge attribute by default. If neither is set, each edge's weight defaults to `1`. 
+To utilize a custom weight property, the user must set the `--storage-properties-on-edges=true` flag.
 - `coloring: bool (default=False)` ➡ If set, use the graph coloring heuristic for effective parallelization.
 - `min_graph_shrink: int  (default=100000)` ➡ The graph coarsening optimization stops upon shrinking the graph to this many nodes.
 - `community_alg_threshold: double  (default=0.000001)` ➡ Controls how long the algorithm iterates. When the gain in modularity
@@ -108,7 +112,7 @@ Computes graph communities over a subgraph using the Louvain method.
 
 {<h4> Output: </h4>}
 
-- `node: Vertex` ➡ Graph node.
+- `node: Vertex` ➡ A graph node for which the algorithm was performed and returned as part of the results.
 - `community_id: int` ➡ Community ID. Defaults to $-1$ if the node does not belong to any community.
 
 {<h4> Usage: </h4>}

pages/advanced-algorithms/available-algorithms/community_detection_online.mdx

@@ -65,7 +65,9 @@ the user should check if that is indeed the case.
 
 {<h4> Input: </h4>}
 
-- `subgraph: Graph` (**OPTIONAL**) ➡ A specific subgraph, which is an [object of type Graph](/advanced-algorithms/run-algorithms#run-procedures-on-subgraph) returned by the `project()` function, on which the algorithm is run.
+- `subgraph: Graph` (**OPTIONAL**) ➡ A specific subgraph, which is an [object of type Graph](/advanced-algorithms/run-algorithms#run-procedures-on-subgraph) returned by the `project()` function, on which the algorithm is run. 
+If subgraph is not specified, the algorithm is computed on the entire graph by default.
+
 - `directed: boolean (default=False)` ➡ Specifies whether the graph is directed. If not set,
   the graph is treated as undirected.
 - `weighted: boolean (default=False)` ➡ Specifies whether the graph is weighted. If not set,
@@ -88,7 +90,7 @@ the user should check if that is indeed the case.
 
 {<h4> Output: </h4>}
 
-- `node: Vertex` ➡ Graph node.
+- `node: Vertex` ➡ A graph node for which the algorithm was performed and returned as part of the results.
 - `community_id: integer` ➡ Community ID. If the node is not associated with any
   community, defaults to $-1$.
 
@@ -108,11 +110,13 @@ are none, defaults to calling `set()` with default parameters.
 
 {<h4> Input: </h4>}
 
-- `subgraph: Graph` (**OPTIONAL**) ➡ A specific subgraph, which is an [object of type Graph](/advanced-algorithms/run-algorithms#run-procedures-on-subgraph) returned by the `project()` function, on which the algorithm is run.
+- `subgraph: Graph` (**OPTIONAL**) ➡ A specific subgraph, which is an [object of type Graph](/advanced-algorithms/run-algorithms#run-procedures-on-subgraph) returned by the `project()` function, on which the algorithm is run. 
+If subgraph is not specified, the algorithm is computed on the entire graph by default.
+
 
 {<h4> Output: </h4>}
 
-- `node: Vertex` ➡ Graph node.
+- `node: Vertex` ➡ A graph node for which the algorithm was performed and returned as part of the results.
 - `community_id: integer` ➡ Community ID. Defaults to $-1$ if the node does not belong to any community.
 
 {<h4> Usage: </h4>}
@@ -131,7 +135,9 @@ changes applied in the latest graph update and returns the results.
 
 {<h4> Input: </h4>}
 
-- `subgraph: Graph` (**OPTIONAL**) ➡ A specific subgraph, which is an [object of type Graph](/advanced-algorithms/run-algorithms#run-procedures-on-subgraph) returned by the `project()` function, on which the algorithm is run.
+- `subgraph: Graph` (**OPTIONAL**) ➡ A specific subgraph, which is an [object of type Graph](/advanced-algorithms/run-algorithms#run-procedures-on-subgraph) returned by the `project()` function, on which the algorithm is run. 
+If subgraph is not specified, the algorithm is computed on the entire graph by default.
+
 - `createdVertices: mgp.List[mgp.Vertex]` ➡ Nodes created in the latest graph
   update.
 - `createdEdges: mgp.List[mgp.Edge]` ➡ Relationships created in the latest graph update.
@@ -144,7 +150,7 @@ changes applied in the latest graph update and returns the results.
 
 {<h4> Output: </h4>}
 
-- `node: Vertex` ➡ Graph node.
+- `node: Vertex` ➡ A graph node for which the algorithm was performed and returned as part of the results.
 - `community_id: integer` ➡ Community ID. If the node is not associated with any
   community, defaults to $-1$.
 
@@ -182,7 +188,9 @@ Resets the algorithm to its initial state.
 
 {<h4> Input: </h4>}
 
-- `subgraph: Graph` (**OPTIONAL**) ➡ A specific subgraph, which is an [object of type Graph](/advanced-algorithms/run-algorithms#run-procedures-on-subgraph) returned by the `project()` function, on which the algorithm is run.
+- `subgraph: Graph` (**OPTIONAL**) ➡ A specific subgraph, which is an [object of type Graph](/advanced-algorithms/run-algorithms#run-procedures-on-subgraph) returned by the `project()` function, on which the algorithm is run. 
+If subgraph is not specified, the algorithm is computed on the entire graph by default.
+
 
 {<h4> Output: </h4>}