Support projection directionality as param

CHANGELOG.md

@@ -4,6 +4,7 @@
 1. Add a new get_relationship_properties_keys tool.
 2. Add targetNode filtering for longest_path.
 3. Add support for similarity algorithms.
+4. Add relationship directionality used by graph projection as an optional parameter to all appropriate algorithms. This include all algorithms that support both directed and undirected graphs and behave differently.
 
 ### Bug Fixes
 1. Return node names in several path algorithms that only returned node ids.

mcp_server/src/mcp_server_neo4j_gds/centrality_algorithm_handlers.py

@@ -13,8 +13,8 @@
 
 
 class ArticleRankHandler(AlgorithmHandler):
-    def article_rank(self, **kwargs):
-        with projected_graph(self.gds) as G:
+    def article_rank(self, undirected: bool = False, **kwargs):
+        with projected_graph(self.gds, undirected=undirected) as G:
             # If any optional parameter is not None, use that parameter
             args = locals()
             params = {
@@ -46,6 +46,7 @@ def article_rank(self, **kwargs):
 
     def execute(self, arguments: Dict[str, Any]) -> Any:
         return self.article_rank(
+            undirected=arguments.get("undirected", False),
             nodes=arguments.get("nodes"),
             nodeIdentifierProperty=arguments.get("nodeIdentifierProperty"),
             sourceNodes=arguments.get("sourceNodes"),
@@ -75,8 +76,8 @@ def execute(self, arguments: Dict[str, Any]) -> Any:
 
 
 class BetweennessCentralityHandler(AlgorithmHandler):
-    def betweenness_centrality(self, **kwargs):
-        with projected_graph(self.gds) as G:
+    def betweenness_centrality(self, undirected: bool = False, **kwargs):
+        with projected_graph(self.gds, undirected=undirected) as G:
             params = {
                 k: v
                 for k, v in kwargs.items()
@@ -99,6 +100,7 @@ def betweenness_centrality(self, **kwargs):
 
     def execute(self, arguments: Dict[str, Any]) -> Any:
         return self.betweenness_centrality(
+            undirected=arguments.get("undirected", False),
             nodes=arguments.get("nodes"),
             nodeIdentifierProperty=arguments.get("nodeIdentifierProperty"),
             samplingSize=arguments.get("samplingSize"),
@@ -124,13 +126,13 @@ def bridges(self, **kwargs):
 
     def execute(self, arguments: Dict[str, Any]) -> Any:
         return self.bridges(
-            nodeIdentifierProperty=arguments.get("nodeIdentifierProperty")
+            nodeIdentifierProperty=arguments.get("nodeIdentifierProperty"),
         )
 
 
 class CELFHandler(AlgorithmHandler):
-    def celf(self, **kwargs):
-        with projected_graph(self.gds) as G:
+    def celf(self, undirected: bool = False, **kwargs):
+        with projected_graph(self.gds, undirected=undirected) as G:
             params = {
                 k: v
                 for k, v in kwargs.items()
@@ -147,6 +149,7 @@ def celf(self, **kwargs):
 
     def execute(self, arguments: Dict[str, Any]) -> Any:
         return self.celf(
+            undirected=arguments.get("undirected", False),
             seedSetSize=arguments.get("seedSetSize"),
             monteCarloSimulations=arguments.get("monteCarloSimulations"),
             propagationProbability=arguments.get("propagationProbability"),
@@ -155,8 +158,8 @@ def execute(self, arguments: Dict[str, Any]) -> Any:
 
 
 class ClosenessCentralityHandler(AlgorithmHandler):
-    def closeness_centrality(self, **kwargs):
-        with projected_graph(self.gds) as G:
+    def closeness_centrality(self, undirected: bool = False, **kwargs):
+        with projected_graph(self.gds, undirected=undirected) as G:
             params = {
                 k: v
                 for k, v in kwargs.items()
@@ -174,20 +177,20 @@ def closeness_centrality(self, **kwargs):
         centrality = filter_identifiers(
             self.gds, node_identifier_property, node_names, centrality
         )
-
         return centrality
 
     def execute(self, arguments: Dict[str, Any]) -> Any:
         return self.closeness_centrality(
+            undirected=arguments.get("undirected", False),
             nodes=arguments.get("nodes"),
             nodeIdentifierProperty=arguments.get("nodeIdentifierProperty"),
             useWassermanFaust=arguments.get("useWassermanFaust"),
         )
 
 
 class DegreeCentralityHandler(AlgorithmHandler):
-    def degree_centrality(self, **kwargs):
-        with projected_graph(self.gds) as G:
+    def degree_centrality(self, undirected: bool = False, **kwargs):
+        with projected_graph(self.gds, undirected=undirected) as G:
             params = {
                 k: v
                 for k, v in kwargs.items()
@@ -210,15 +213,16 @@ def degree_centrality(self, **kwargs):
 
     def execute(self, arguments: Dict[str, Any]) -> Any:
         return self.degree_centrality(
+            undirected=arguments.get("undirected", False),
             nodes=arguments.get("nodes"),
             nodeIdentifierProperty=arguments.get("nodeIdentifierProperty"),
             orientation=arguments.get("orientation"),
         )
 
 
 class EigenvectorCentralityHandler(AlgorithmHandler):
-    def eigenvector_centrality(self, **kwargs):
-        with projected_graph(self.gds) as G:
+    def eigenvector_centrality(self, undirected: bool = False, **kwargs):
+        with projected_graph(self.gds, undirected=undirected) as G:
             params = {
                 k: v
                 for k, v in kwargs.items()
@@ -250,6 +254,7 @@ def eigenvector_centrality(self, **kwargs):
 
     def execute(self, arguments: Dict[str, Any]) -> Any:
         return self.eigenvector_centrality(
+            undirected=arguments.get("undirected", False),
             nodes=arguments.get("nodes"),
             nodeIdentifierProperty=arguments.get("nodeIdentifierProperty"),
             maxIterations=arguments.get("maxIterations"),
@@ -261,8 +266,8 @@ def execute(self, arguments: Dict[str, Any]) -> Any:
 
 
 class PageRankHandler(AlgorithmHandler):
-    def pagerank(self, **kwargs):
-        with projected_graph(self.gds) as G:
+    def pagerank(self, undirected: bool = False, **kwargs):
+        with projected_graph(self.gds, undirected=undirected) as G:
             params = {
                 k: v
                 for k, v in kwargs.items()
@@ -293,6 +298,7 @@ def pagerank(self, **kwargs):
 
     def execute(self, arguments: Dict[str, Any]) -> Any:
         return self.pagerank(
+            undirected=arguments.get("undirected", False),
             nodes=arguments.get("nodes"),
             nodeIdentifierProperty=arguments.get("nodeIdentifierProperty"),
             sourceNodes=arguments.get("sourceNodes"),
@@ -303,8 +309,8 @@ def execute(self, arguments: Dict[str, Any]) -> Any:
 
 
 class HarmonicCentralityHandler(AlgorithmHandler):
-    def harmonic_centrality(self, **kwargs):
-        with projected_graph(self.gds) as G:
+    def harmonic_centrality(self, undirected: bool = False, **kwargs):
+        with projected_graph(self.gds, undirected=undirected) as G:
             centrality = self.gds.closeness.harmonic.stream(G)
 
         # Add node names to the results if nodeIdentifierProperty is provided
@@ -320,14 +326,15 @@ def harmonic_centrality(self, **kwargs):
 
     def execute(self, arguments: Dict[str, Any]) -> Any:
         return self.harmonic_centrality(
+            undirected=arguments.get("undirected", False),
             nodes=arguments.get("nodes"),
             nodeIdentifierProperty=arguments.get("nodeIdentifierProperty"),
         )
 
 
 class HITSHandler(AlgorithmHandler):
-    def hits(self, **kwargs):
-        with projected_graph(self.gds) as G:
+    def hits(self, undirected: bool = False, **kwargs):
+        with projected_graph(self.gds, undirected=undirected) as G:
             params = {
                 k: v
                 for k, v in kwargs.items()
@@ -349,6 +356,7 @@ def hits(self, **kwargs):
 
     def execute(self, arguments: Dict[str, Any]) -> Any:
         return self.hits(
+            undirected=arguments.get("undirected", False),
             nodes=arguments.get("nodes"),
             nodeIdentifierProperty=arguments.get("nodeIdentifierProperty"),
             hitsIterations=arguments.get("hitsIterations"),

mcp_server/src/mcp_server_neo4j_gds/centrality_algorithm_specs.py

@@ -60,6 +60,10 @@
                     "Supported values are None, MinMax, Max, Mean, Log, and StdScore. "
                     "To apply scaler-specific configuration, use the Map syntax: {scaler: 'name', ...}.",
                 },
+                "undirected": {
+                    "type": "boolean",
+                    "description": "Whether to treat the graph as undirected or not. Default is false (directed).",
+                },
             },
             "required": [],
         },
@@ -101,6 +105,10 @@
                     "type": "string",
                     "description": "Property of the relationship to use for weighting. If not specified, all relationships are treated equally.",
                 },
+                "undirected": {
+                    "type": "boolean",
+                    "description": "Whether to treat the graph as undirected or not. Default is false (directed).",
+                },
             },
             "required": [],
         },
@@ -142,6 +150,10 @@
                     "type": "string",
                     "description": "Property name to use for identifying nodes (e.g., 'name', 'Name', 'title'). Use get_node_properties_keys to find available properties.",
                 },
+                "undirected": {
+                    "type": "boolean",
+                    "description": "Whether to treat the graph as undirected or not. Default is false (directed).",
+                },
             },
             "required": ["seedSetSize"],
         },
@@ -167,6 +179,10 @@
                     "type": "boolean",
                     "description": "If true, uses the Wasserman-Faust formula for closeness centrality. ",
                 },
+                "undirected": {
+                    "type": "boolean",
+                    "description": "Whether to treat the graph as undirected or not. Default is false (directed).",
+                },
             },
             "required": [],
         },
@@ -190,6 +206,10 @@
                     "type": "string",
                     "description": "The orientation used to compute node degrees. Supported orientations are NATURAL (for out-degree), REVERSE (for in-degree) and UNDIRECTED (for both in-degree and out-degree) ",
                 },
+                "undirected": {
+                    "type": "boolean",
+                    "description": "Whether to treat the graph as undirected or not. Default is false (directed).",
+                },
             },
             "required": [],
         },
@@ -256,6 +276,10 @@
                     "Supported values are None, MinMax, Max, Mean, Log, and StdScore. "
                     "To apply scaler-specific configuration, use the Map syntax: {scaler: 'name', ...}.",
                 },
+                "undirected": {
+                    "type": "boolean",
+                    "description": "Whether to treat the graph as undirected or not. Default is false (directed).",
+                },
             },
         },
     ),
@@ -307,6 +331,10 @@
                         },
                     ],
                 },
+                "undirected": {
+                    "type": "boolean",
+                    "description": "Whether to treat the graph as undirected or not. Default is false (directed).",
+                },
             },
             "required": [],
         },
@@ -327,6 +355,10 @@
                     "type": "string",
                     "description": "Property name to use for identifying nodes (e.g., 'name', 'Name', 'title'). Use get_node_properties_keys to find available properties.",
                 },
+                "undirected": {
+                    "type": "boolean",
+                    "description": "Whether to treat the graph as undirected or not. Default is false (directed).",
+                },
             },
             "required": [],
         },
@@ -365,6 +397,10 @@
                     "enum": ["AUTO", "RANGE", "DEGREE"],
                     "description": "The partitioning scheme used to divide the work between threads. Available options are AUTO, RANGE, DEGREE.",
                 },
+                "undirected": {
+                    "type": "boolean",
+                    "description": "Whether to treat the graph as undirected or not. Default is false (directed).",
+                },
             },
             "required": [],
         },