Create app.0.py

Satisfied with version 0. Want to add Laplacian centrality

app.0.py

@@ -0,0 +1,99 @@
+import streamlit as st
+from datasets import load_dataset
+import networkx as nx
+import numpy as np
+dataset = load_dataset("roneneldan/TinyStories")
+
+st.write(dataset['train'][10]['text'])
+
+threshhold = st.slider('Threshhold',0.0,1.0,step=0.1)
+
+#-------------------------------------------------------------
+#-------------------------------------------------------------
+
+from sentence_transformers import SentenceTransformer, util
+model = SentenceTransformer('all-MiniLM-L6-v2')
+
+# Sentences from the data set
+#sentences = [item['text'] for item in dataset['train'][:10]]
+
+#sentences = [dataset['train'][0],dataset['train'][1],dataset['train'][2]]
+sentences = [dataset['train'][ii] for ii in range(10)]
+
+#Compute embedding 
+embeddings = model.encode(sentences, convert_to_tensor=True)
+
+#Compute cosine-similarities
+cosine_scores = util.cos_sim(embeddings, embeddings)
+
+# creating adjacency matrix
+A = np.zeros((len(sentences),len(sentences)))
+
+#Output the pairs with their score
+for i in range(len(sentences)):
+    for j in range(i):
+        #st.write("{} \t\t {} \t\t Score: {:.4f}".format(sentences[i], sentences[j], cosine_scores[i][j]))
+        A[i][j] = cosine_scores[i][j]
+        A[j][i] = cosine_scores[i][j]
+
+#G = nx.from_numpy_array(A)
+G = nx.from_numpy_array(cosine_scores.numpy()>threshhold)
+
+
+#-------------------------------------------------------------
+#-------------------------------------------------------------
+# ego_graph.py
+# An example of how to plot a node's ego network 
+# (egonet). This indirectly showcases slightly more involved 
+# interoperability between streamlit-agraph and networkx.
+
+# An egonet can be # created from (almost) any network (graph),
+# and exemplifies the # concept of a subnetwork (subgraph):
+# A node's egonet is the (sub)network comprised of the focal node 
+# and all the nodes to whom it is adjacent. The edges included
+# in the egonet are those nodes are both included in the aforementioned
+# nodes. 
+
+# Use the following command to launch the app
+# streamlit run <path-to-script>.py
+
+# standard library dependencies
+from operator import itemgetter
+
+# external dependencies
+import networkx as nx
+from streamlit_agraph import agraph, Node, Edge, Config
+
+# First create a graph using the Barabasi-Albert model
+n = 2000
+m = 2
+#G = nx.generators.barabasi_albert_graph(n, m, seed=2023)
+
+# Then find the node with the largest degree; 
+# This node's egonet will be the focus of this example.
+node_and_degree = G.degree()
+most_connected_node = sorted(G.degree, key=lambda x: x[1], reverse=True)[0]
+degree = G.degree(most_connected_node)
+
+# Create egonet for the focal node
+hub_ego = nx.ego_graph(G, most_connected_node[0])
+
+# Now create the equivalent Node and Edge lists
+nodes = [Node(title=str(sentences[i]['text']), id=i, label='node_'+str(i), size=20) for i in hub_ego.nodes]
+edges = [Edge(source=i, target=j, type="CURVE_SMOOTH") for (i,j) in G.edges
+        if i in hub_ego.nodes and j in hub_ego.nodes]
+
+
+config = Config(width=500, 
+                height=500, 
+                directed=True,
+                nodeHighlightBehavior=False, 
+                highlightColor="#F7A7A6", # or "blue"
+                collapsible=False,
+                node={'labelProperty':'label'},
+                # **kwargs e.g. node_size=1000 or node_color="blue"
+                ) 
+
+return_value = agraph(nodes=nodes, 
+                      edges=edges, 
+                      config=config)