[Question]: How can I visualize triplets generated using the property index graph?

[goodrahstar]

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• [X] I have searched both the documentation and discord for an answer.

Question

I'm working on visualizing a knowledge graph that I created using a property index graph. While I can easily extract and visualize the NetworkX graph when using a knowledge graph index, I'm unable to achieve the same with a property index graph. Looking for help with this visualization issue.

I'll share the code I used for building and visualizing the knowledge graph below.

kg_index = KnowledgeGraphIndex()

g = kg_index.get_networkx_graph(1000)
net.from_nx(g)
net.show(output_path,notebook=False)

[dosubot[bot]]

To visualize triplets generated using a property index graph, you can use the pyvis library to create a visual representation of the graph, similar to how you would with a NetworkX graph. Here's a general approach you can follow:

1. Convert the Property Graph to a NetworkX Graph: If your property index graph is stored in a SimplePropertyGraphStore, you can use a helper function to save it as a NetworkX graph.

index.property_graph_store.save_networkx_graph(name="./kg.html")

2. Visualize with Pyvis: Once you have the NetworkX graph, you can visualize it using pyvis as you would with a knowledge graph index.

from pyvis.network import Network

# Assuming `g` is your NetworkX graph
net = Network(notebook=True, cdn_resources="in_line", directed=True)
net.from_nx(g)  # Convert the NetworkX graph to a Pyvis network
net.show("example.html")  # Visualize the graph in an HTML file

This approach allows you to visualize the property index graph in a similar manner to the knowledge graph index ^[1].

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[goodrahstar]

I'm looking for guidance on extracting metrics from a property index graph using NetworkX - specifically the node/entity counts and various matrix representations. What's the best way to convert my property index graph into a NetworkX format to leverage its full functionality? Could you outline the export process?

[dosubot[bot]]

To extract metrics like node/entity counts and various matrix representations from a property index graph using NetworkX, you first need to convert your property index graph into a NetworkX format. Here's how you can do it:

1. Convert the Property Index Graph to NetworkX: If your property index graph is stored in a SimplePropertyGraphStore, you can use the save_networkx_graph method to convert it to a NetworkX graph. This method creates a directed graph and adds nodes and edges based on the nodes and triplets in the graph.

   index.property_graph_store.save_networkx_graph(name="./kg.html")

   This will save the graph as an HTML file, but you can modify the method to return a NetworkX graph object for further analysis.

2. Extract Metrics Using NetworkX: Once you have the NetworkX graph, you can use NetworkX's built-in functions to extract various metrics:

   • Node/Entity Counts: Use G.number_of_nodes() to get the count of nodes/entities.
   • Matrix Representations: You can derive adjacency matrices, incidence matrices, etc., using functions like nx.adjacency_matrix(G) or nx.incidence_matrix(G).

By following these steps, you can leverage NetworkX's full functionality to analyze your property index graph.

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[goodrahstar]

How to do this?

index.property_graph_store.save_networkx_graph(name="./kg.html")

"This will save the graph as an HTML file, but you can modify the method to return a NetworkX graph object for further analysis. "

[dosubot[bot]]

To modify the save_networkx_graph method to return a NetworkX graph object instead of saving it as an HTML file, you can adjust the method to simply return the G object. Here is how you can modify the method:

def get_networkx_graph(self, limit: int = 100) -> nx.Graph:
    """Return the NetworkX graph object."""
    try:
        import networkx as nx
    except ImportError:
        raise ImportError(
            "Please install networkx to visualize the graph: `pip install networkx`"
        )

    g = nx.Graph()
    subjs = list(self.index_struct.table.keys())

    # add edges
    rel_map = self._graph_store.get_rel_map(subjs=subjs, depth=1, limit=limit)

    added_nodes = set()
    for keyword in rel_map:
        for path in rel_map[keyword]:
            subj = keyword
            for i in range(0, len(path), 2):
                if i + 2 >= len(path):
                    break

                if subj not in added_nodes:
                    g.add_node(subj)
                    added_nodes.add(subj)

                rel = path[i + 1]
                obj = path[i + 2]

                g.add_edge(subj, obj, label=rel, title=rel)
                subj = obj
    return g

This modified method, get_networkx_graph, constructs the NetworkX graph and returns it directly ^[1][2].

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