diff --git a/custom-recipes/ego-graph-from-nodes/recipe.json b/custom-recipes/ego-graph-from-nodes/recipe.json
new file mode 100644
index 0000000..3025633
--- /dev/null
+++ b/custom-recipes/ego-graph-from-nodes/recipe.json
@@ -0,0 +1,70 @@
+{
+  "meta": {
+    "label": "Ego graph from nodes",
+    "description": "Returns induced subgraph of neighbors centered at selected nodes within a given radius",
+    "icon": "icon-link"
+  },
+
+  "kind": "PYTHON",
+
+  "selectableFromDataset" : "Input Dataset",
+  "inputRoles" : [
+    {
+      "name": "Input Dataset",
+      "arity": "UNARY",
+      "required": true,
+      "acceptsDataset": true
+    }
+  ],
+  "outputRoles" : [
+    {
+      "name": "Output Dataset",
+      "arity": "UNARY",
+      "required": true,
+      "acceptsDataset": true
+    }
+  ],
+
+  "params": [
+    {
+      "name": "source_nodes",
+      "label": "Source nodes",
+      "type": "COLUMN",
+      "columnRole":"Input Dataset"
+    },
+    {
+      "name": "target_nodes",
+      "label": "Target nodes",
+      "type": "COLUMN",
+      "columnRole":"Input Dataset"
+    },
+    {
+      "name": "edges_label",
+      "label": "Edges label",
+      "type": "COLUMNS",
+      "columnRole":"Input Dataset"
+    },
+    {
+      "name": "weighted",
+      "label": "Weighted graph",
+      "type": "BOOLEAN",
+      "defaultValue": false,
+      "description": ""
+    },
+    {
+      "name": "nodes",
+      "label": "Nodes",
+      "type": "STRINGS",
+      "description": ""
+    },
+    {
+      "name": "ego_graph_radius",
+      "label": "Ego graph radius",
+      "type": "INT",
+      "defaultValue": 2,
+      "description": ""
+    }
+  ],
+
+  "resourceKeys": []
+}
diff --git a/custom-recipes/ego-graph-from-nodes/recipe.py b/custom-recipes/ego-graph-from-nodes/recipe.py
new file mode 100644
index 0000000..0719c75
--- /dev/null
+++ b/custom-recipes/ego-graph-from-nodes/recipe.py
@@ -0,0 +1,72 @@
+# -*- coding: utf-8 -*-
+from dataiku.customrecipe import get_recipe_config
+from graph_analytics_utils import get_input_dataset, get_output_dataset
+from graph_analytics_constants import Constants
+import pandas as pd
+import networkx as nx
+from networkx.algorithms import union
+import logging
+import time
+
+def create_ego_graph(graph, node, radius=1, predecessors=False):
+  try:
+    # Create a directed ego-graph from node to get it's successors
+    node_ego_graph = nx.ego_graph(graph, node, radius)
+
+    # Reverse graph to get level 1 predecessors
+    if predecessors:
+      tmp_graph = graph.reverse()
+      predecessors_ego_graph = nx.ego_graph(tmp_graph, node, radius=1)
+      predecessors_ego_graph = predecessors_ego_graph.reverse()
+      node_ego_graph.update(predecessors_ego_graph)
+
+    return node_ego_graph
+  
+  # Return an empty directed graph if node is not found in graph
+  except:
+    logging.warn("Ego graph - Node " + node + " not found in graph")
+    return nx.DiGraph()
+
+
+# Read recipe config
+input_dataset = get_input_dataset('Input Dataset')
+output_dataset = get_output_dataset('Output Dataset')
+
+recipe_config = get_recipe_config()
+
+# List of necessary columns
+columns = []
+columns.append(recipe_config['source_nodes'])
+columns.append(recipe_config['target_nodes'])
+for col in recipe_config['edges_label']:
+  columns.append(col)
+
+# Recipe input
+input_df = input_dataset.get_dataframe(columns=columns)
+logging.info("Ego graph - Dataset loaded")
+
+# Delete nulls
+input_df = input_df[(input_df[recipe_config['source_nodes']].notnull()) & (input_df[recipe_config['target_nodes']].notnull())]
+logging.info("Ego graph - Null values removed")
+
+# Dedup
+deduplicated_df = input_df.groupby(columns).size().reset_index().rename(columns={0: 'w'})
+logging.info("Ego graph - Deduplicated dataset created")
+
+# Creating the directed graph
+graph = nx.from_pandas_edgelist(deduplicated_df, recipe_config['source_nodes'], recipe_config['target_nodes'], recipe_config['edges_label'], nx.DiGraph)
+logging.info("Base NetworkX graph created")
+
+node_ego_graph = nx.DiGraph()
+for node in recipe_config["nodes"]:
+    node_ego_graph.update(create_ego_graph(graph, node, recipe_config["ego_graph_radius"], True))
+
+# Write output dataframe
+output_df = pd.DataFrame(list(node_ego_graph.edges(data=True)))
+output_df.columns = [recipe_config['source_nodes'], recipe_config['target_nodes'], "Edges labels"] 
+
+logging.info("Ego graph - Ego graph computed")
+
+output_dataset.write_with_schema(output_df)
+
+