{"id":76091,"date":"2024-05-19T23:01:21","date_gmt":"2024-05-20T03:01:21","guid":{"rendered":"https:\/\/bangla.sitestree.com\/spearman-correlation-coefficient-and-graph-mining\/"},"modified":"2024-05-19T23:01:21","modified_gmt":"2024-05-20T03:01:21","slug":"spearman-correlation-coefficient-and-graph-mining","status":"publish","type":"post","link":"http:\/\/bangla.sitestree.com\/?p=76091","title":{"rendered":"Spearman Correlation Coefficient and Graph Mining"},"content":{"rendered":"

#!\/usr\/bin\/env python<\/p>\n

coding: utf-8<\/h1>\n

# 3rd Model: Deepgraph CNN: Stock Price Prediction using DeepGraphCNN Neural Networks. It includes GCN layers and CNN layers. I have added an MLP at the last layer to predict stock prices.<\/h1>\n

#<\/p>\n

# Input graphs were created for spearman, Spearman, and Kendal Tau correlations\/coefficients from historical stock prices. Also, another graph is created based on financial news articles.<\/h1>\n

#<\/p>\n

# For the sake of making execution easier (and at once), I have kept multiple approaches (spearman, Spearman, and Kendal Tau, News Based) in the same file. One big code file can be difficult to handle; is done just for making execution easier.<\/h1>\n

#<\/p>\n

# Because I initially tried separately and brought the code together, some code might be a bit redundant\/repeating. I may have done some cleaning.<\/h1>\n

#<\/p>\n

# An use case of DeepGraphCNN for Node Classification<\/h1>\n

# https:\/\/stellargraph.readthedocs.io\/en\/latest\/demos\/graph-classification\/dgcnn-graph-classification.html<\/a><\/h1>\n

#<\/p>\n

# Import Libraries<\/h1>\n

In[1]:<\/h1>\n

import libraries<\/h1>\n

import os
\nimport pandas as pd
\nimport math<\/p>\n

In[2]:<\/h1>\n

Import Libraries for Graph, GNN, and GCN<\/h1>\n

import stellargraph as sg
\nfrom stellargraph import StellarGraph
\nfrom stellargraph.layer import DeepGraphCNN
\nfrom stellargraph.mapper import FullBatchNodeGenerator
\nfrom stellargraph.mapper import PaddedGraphGenerator
\nfrom stellargraph.layer import GCN<\/p>\n

In[3]:<\/h1>\n

Machine Learnig related library Imports<\/h1>\n

from tensorflow.keras import layers, optimizers, losses, metrics, Model
\nfrom sklearn import preprocessing, model_selection
\nfrom IPython.display import display, HTML
\nimport matplotlib.pyplot as plt
\nget_ipython().run_line_magic(‘matplotlib’, ‘inline’)
\nfrom tensorflow.keras.layers import Dense, Conv1D, MaxPool1D, Dropout, Flatten
\nfrom tensorflow import keras<\/p>\n

In[4]:<\/h1>\n

If we want to drop NAN column or row wise for stock price data<\/h1>\n

I did not need to use this options that much<\/h1>\n

drop_cols_with_na = 1
\ndrop_rows_with_na = 1<\/p>\n

# Dataset: Using 30 companies from the Fortune 500 companies (the paper used these stocks)<\/h1>\n

In[5]:<\/h1>\n

df_s = pd.DataFrame();
\ndata_file = "per-day-fortune-30-company-stock-price-data.csv";
\ndf_s = pd.read_csv(".\/data\/" + data_file, low_memory = False);
\ndf_s.head()<\/p>\n

In[6]:<\/h1>\n

You can see ANTM stock price data is empty<\/h1>\n

# Cure data such as replace missing\/null values, use correct data type, sort by date (not really required)<\/h1>\n

In[7]:<\/h1>\n

convert Date field to be a Date Type<\/h1>\n

df_s["Date"] = df_s["Date"].astype(‘datetime64[ns]’)<\/p>\n

Sort data by date although this is no longer needed as data already is sorted when I generated data<\/h1>\n

df_s = df_s.sort_values( by = ['Ticker','Date'], ascending = True )<\/h1>\n

df_s = df_s.sort_values( by = ‘Date’, ascending = True )
\ndf_s.head()<\/p>\n

In[8]:<\/h1>\n

https:\/\/pandas.pydata.org\/pandas-docs\/stable\/reference\/api\/pandas.DataFrame.interpolate.html<\/a><\/h1>\n

df_s_transpose = df_s<\/p>\n

try:
\ndf_s_transpose = df_s_transpose.interpolate(inplace = False)
\nexcept:
\nprint("An exception occurred. Operation ignored")
\nexit<\/p>\n

check if any value is null<\/h1>\n

df_s_transpose.isnull().values.any()<\/p>\n

check if any column (axis=1) is null<\/h1>\n

df_s_transpose[df_s_transpose.isna().any(axis = 1)]<\/p>\n

In[9]:<\/h1>\n

df_s_transpose<\/p>\n

In[10]:<\/h1>\n

df_s_transpose = df_s<\/h1>\n

if drop_cols_with_na == 1:
\ndf_s_transpose = df_s_transpose.dropna(axis = 1);<\/p>\n

print(df_s_transpose.shape)
\ndf_s_transpose.head()<\/p>\n

In[11]:<\/h1>\n

further check and verify<\/h1>\n

df_s_transpose.isnull().values.any()
\ndf_s_transpose[df_s_transpose.isna().any( axis = 1 )]<\/p>\n

In[12]:<\/h1>\n

making the date column as the index column for the dataset<\/h1>\n

df_s_transpose.index = df_s_transpose['Date']<\/h1>\n

df_s_transpose.index = df_s_transpose.index.astype(‘datetime64[ns]’)<\/p>\n

# spearman Correlation Coefficient<\/h1>\n

In[13]:<\/h1>\n

df_s_transpose_spearman = df_s_transpose.corr(method = ‘spearman’, numeric_only = True)
\ndf_s_transpose_spearman<\/p>\n

# spearman Correlation Coefficient based Adjacency Graph Matrix<\/h1>\n

In[14]:<\/h1>\n

df_s_transpose_spearman[df_s_transpose_spearman >= 0.4] = 1
\ndf_s_transpose_spearman[df_s_transpose_spearman < 0.4] = 0
\ndf_s_transpose_spearman<\/p>\n

In[15]:<\/h1>\n

make the diagonal element to be zero. No self loop\/edge<\/h1>\n

import numpy as np
\nnp.fill_diagonal(df_s_transpose_spearman.values, 0)
\ndf_s_transpose_spearman<\/p>\n

Create and visualize the Graphs<\/h1>\n

In[17]:<\/h1>\n

import networkx as nx
\nGraph_spearman = nx.Graph(df_s_transpose_spearman)<\/p>\n

In[18]:<\/h1>\n

nx.draw_networkx(Graph_spearman, pos = nx.circular_layout( Graph_spearman ), node_color = ‘r’, edge_color = ‘b’)<\/p>\n

# Experiment, we will divide the data into train, test, and validation graphs<\/h1>\n

In[19]:<\/h1>\n

df_s_transpose.corr(method = ‘spearman’, numeric_only = True)
\n#df_s_transpose[[{1,2,3}]]
\n#df_s_transpose.iloc[:, 0:10]<\/p>\n

In[20]:<\/h1>\n

Train Graph<\/h1>\n

In[21]:<\/h1>\n

df_s_spearman_train = df_s_transpose.iloc[:, 0:15]
\ndf_s_transpose_spearman_train = df_s_spearman_train.corr(method = ‘spearman’, numeric_only = True)
\nnp.fill_diagonal(df_s_transpose_spearman_train.values, 0)<\/p>\n

df_s_transpose_spearman_train[df_s_transpose_spearman_train >= 0.4] = 1
\ndf_s_transpose_spearman_train[df_s_transpose_spearman_train < 0.4] = 0
\ndf_s_transpose_spearman_train<\/p>\n

df_s_transpose_spearman_train<\/p>\n

# Test Graph<\/h1>\n

In[22]:<\/h1>\n

df_s_spearman_test = df_s_transpose.iloc[:, 15:] #df_s_transpose.iloc[:, 15:23]
\ndf_s_transpose_spearman_test = df_s_spearman_test.corr(method = ‘spearman’, numeric_only = True)
\nnp.fill_diagonal(df_s_transpose_spearman_test.values, 0)<\/p>\n

df_s_transpose_spearman_train[df_s_transpose_spearman_test >= 0.4] = 1
\ndf_s_transpose_spearman_train[df_s_transpose_spearman_test < 0.4] = 0
\ndf_s_transpose_spearman_test<\/p>\n

# Validation Graph<\/h1>\n

In[23]:<\/h1>\n

df_s_spearman_validation = df_s_transpose.iloc[:, 15:] #df_s_transpose.iloc[:, 23:]
\ndf_s_transpose_spearman_validation = df_s_spearman_validation.corr(method = ‘spearman’, numeric_only = True)
\nnp.fill_diagonal(df_s_transpose_spearman_validation.values, 0)
\ndf_s_transpose_spearman_validation<\/p>\n

df_s_transpose_spearman_validation[df_s_transpose_spearman_validation >= 0.4] = 1
\ndf_s_transpose_spearman_validation[df_s_transpose_spearman_validation < 0.4] = 0
\ndf_s_transpose_spearman_validation<\/p>\n

In[24]:<\/h1>\n

graph_spearman_train = nx.Graph(df_s_transpose_spearman_train)
\ngraph_spearman_test = nx.Graph(df_s_transpose_spearman_test)
\ngraph_spearman_validation = nx.Graph(df_s_transpose_spearman_validation)<\/p>\n

nx.draw_networkx(graph_spearman_train, pos = nx.circular_layout( graph_spearman_train ), node_color = ‘r’, edge_color = ‘b’)<\/p>\n

In[25]:<\/h1>\n

df_s_spearman_train.corr(numeric_only = True)<\/p>\n

In[26]:<\/h1>\n

nx.draw_networkx(graph_spearman_test, pos = nx.circular_layout( graph_spearman_test ), node_color = ‘r’, edge_color = ‘b’)<\/p>\n

In[27]:<\/h1>\n

nx.draw_networkx(graph_spearman_validation, pos = nx.circular_layout( graph_spearman_validation ), node_color = ‘r’, edge_color = ‘b’)<\/p>\n

# Create GCN layer. spearman<\/h1>\n

# Find all stocks = nodes<\/h1>\n

In[28]:<\/h1>\n

improvement: make sure only stocks\/nodes that are in the graph are taken<\/h1>\n

all_stock_nodes = df_s_transpose_spearman.index.to_list()
\nall_stock_nodes[:5]<\/p>\n

# Find all edges between nodes<\/h1>\n

#<\/p>\n

This may need adjustment to reflect train, test, validation graphs<\/h1>\n

In[29]:<\/h1>\n

source = [];
\ntarget = [];
\nedge_feature = [];<\/p>\n

for aStock in all_stock_nodes:
\nfor anotherStock in all_stock_nodes:
\nif df_s_transpose_spearman[aStock][anotherStock] > 0:
\n#print(df_s_transpose_spearman[aStock][anotherStock])
\nsource.append(aStock)
\ntarget.append(anotherStock)
\nedge_feature.append(1)<\/p>\n

edge feature is not required except for news based graph<\/h1>\n

source, target, edge_feature<\/p>\n

# Find all edges in Train, Test, and Validation Graphs<\/h1>\n

In[30]:<\/h1>\n

trainSource = [];
\ntrainTarget = [];
\ntrainEdge_feature = [];
\ntrainNodeList = df_s_transpose_spearman_train.index.to_list();<\/p>\n

testSource = [];
\ntestTarget = [];
\ntestEdge_feature = [];
\ntestNodeList = df_s_transpose_spearman_test.index.to_list();<\/p>\n

validationSource = [];
\nvalidationTarget = [];
\nvalidationEdge_feature = [];
\nvalidationNodeList = df_s_transpose_spearman_validation.index.to_list();<\/p>\n

for aStock in trainNodeList:
\nfor anotherStock in trainNodeList:
\nif df_s_transpose_spearman_train[aStock][anotherStock] > 0:
\n#print(df_s_transpose_spearman[aStock][anotherStock])
\ntrainSource.append(aStock)
\ntrainTarget.append(anotherStock)
\ntrainEdge_feature.append(1)<\/p>\n

for aStock in testNodeList:
\nfor anotherStock in testNodeList:
\nif df_s_transpose_spearman_test[aStock][anotherStock] > 0:
\n#print(df_s_transpose_spearman[aStock][anotherStock])
\ntestSource.append(aStock)
\ntestTarget.append(anotherStock)
\ntestEdge_feature.append(1)<\/p>\n

for aStock in validationNodeList:
\nfor anotherStock in validationNodeList:
\nif df_s_transpose_spearman_validation[aStock][anotherStock] > 0:<\/p>\n

print(df_s_transpose_spearman[aStock][anotherStock])<\/h1>\n

validationSource.append(aStock)
\nvalidationTarget.append(anotherStock)
\nvalidationEdge_feature.append(1)<\/p>\n

edge feature is not required except for news based graph<\/h1>\n

trainSource, trainTarget, trainEdge_feature
\ntestSource, testTarget, testEdge_feature
\nvalidationSource, validationTarget, validationEdge_feature<\/p>\n

# Create variables to create stellar graph<\/h1>\n

# Edges<\/h1>\n

In[31]:<\/h1>\n

https:\/\/stellargraph.readthedocs.io\/en\/stable\/demos\/basics\/loading-pandas.html<\/a><\/h1>\n

spearman_edges = pd.DataFrame(
\n{"source": source, "target": target}
\n)<\/p>\n

spearman_edges_data = pd.DataFrame(
\n{"source": source, "target": target, "edge_feature": edge_feature}
\n)<\/p>\n

https:\/\/stellargraph.readthedocs.io\/en\/stable\/demos\/basics\/loading-pandas.html<\/a><\/h1>\n

spearman_edges_train = pd.DataFrame(
\n{"source": trainSource, "target": trainTarget}
\n)<\/p>\n

spearman_edges_data_train = pd.DataFrame(
\n{"source": trainSource, "target": trainTarget, "edge_feature": trainEdge_feature}
\n)<\/p>\n

spearman_edges_test = pd.DataFrame(
\n{"source": testSource, "target": testTarget}
\n)<\/p>\n

spearman_edges_data_test = pd.DataFrame(
\n{"source": testSource, "target": testTarget, "edge_feature": testEdge_feature}
\n)<\/p>\n

spearman_edges_validation = pd.DataFrame(
\n{"source": validationSource, "target": validationTarget}
\n)<\/p>\n

spearman_edges_train[:10]<\/p>\n

# Have the time series data as part of the nodes<\/h1>\n

# Structure the Feature Matrix so that it can be passed to the GCN<\/h1>\n

In[32]:<\/h1>\n

df_s_transpose_feature = df_s_transpose.reset_index(drop = True, inplace = False)<\/p>\n

df_s_transpose_feature = df_s_transpose_feature.values.tolist()<\/h1>\n

print(df_s_transpose_feature.values.tolist())<\/h1>\n

#df_s_transpose_feature['WY'].values
\ndf_s_transpose_feature['AAPL'].shape, df_s_transpose_feature['AAPL'].values<\/p>\n

In[33]:<\/h1>\n

len(all_stock_nodes)<\/p>\n

In[34]:<\/h1>\n

bring\/assign data to nodes<\/h1>\n

node_Data = [];
\nfor x in all_stock_nodes:
\nnode_Data.append( df_s_transpose_feature[x].values)<\/p>\n

node_Data<\/p>\n

In[35]:<\/h1>\n

convert node data variable into a dataframe so that the data structure is compatible with graph NN<\/h1>\n

spearman_graph_node_data = pd.DataFrame(node_Data, index = all_stock_nodes)
\nspearman_graph_node_data.head()<\/p>\n

In[36]:<\/h1>\n

node_Data[14:15],
\nlen(validationNodeList)
\nlen(testNodeList)<\/p>\n

In[37]:<\/h1>\n

Node time series data based on train, test, validation graph<\/h1>\n

In[38]:<\/h1>\n

convert node data variable into a dataframe so that the data structure is compatible with graph NN<\/h1>\n

spearman_graph_node_data_train = pd.DataFrame(node_Data[0:14], index = trainNodeList)
\nspearman_graph_node_data_train.head()<\/p>\n

spearman_graph_node_data_test = pd.DataFrame(node_Data[14:], index = testNodeList) #pd.DataFrame(node_Data[15:23], index = testNodeList)
\nspearman_graph_node_data_test.head()<\/p>\n

spearman_graph_node_data_validation = pd.DataFrame(node_Data[14:], index = validationNodeList) #pd.DataFrame(node_Data[22:30], index = validationNodeList)
\nspearman_graph_node_data_validation.head()<\/p>\n

In[39]:<\/h1>\n

spearman_graph_node_data_train<\/p>\n

# Graph (stellar) with features as part of Nodes<\/h1>\n

In[40]:<\/h1>\n

Overall<\/h1>\n

spearman_graph_with_node_features = StellarGraph(spearman_graph_node_data, edges = spearman_edges, node_type_default = "corner", edge_type_default = "line")
\nprint(spearman_graph_with_node_features.info<\/a>())<\/p>\n

train nodes<\/h1>\n

spearman_train_graph_with_node_features = StellarGraph(spearman_graph_node_data_train, edges = spearman_edges_train, node_type_default = "corner", edge_type_default = "line")
\nprint(spearman_train_graph_with_node_features.info<\/a>())<\/p>\n

test<\/h1>\n

spearman_test_graph_with_node_features = StellarGraph(spearman_graph_node_data_test, edges = spearman_edges_test, node_type_default = "corner", edge_type_default = "line")
\nprint(spearman_test_graph_with_node_features.info<\/a>())<\/p>\n

validation<\/h1>\n

spearman_validation_graph_with_node_features = StellarGraph(spearman_graph_node_data_validation, edges = spearman_edges_validation, node_type_default = "corner", edge_type_default = "line")
\nprint(spearman_validation_graph_with_node_features.info<\/a>())<\/p>\n

# Adapting everything for DeepGraphCNN<\/h1>\n

In[41]:<\/h1>\n

spearman_graph_node_data.iloc[0:15, :]<\/p>\n

# Graphs to be jused for DeepGraphCNN<\/h1>\n

In[42]:<\/h1>\n

graphs = list()
\n#graphs.append(spearman_graph_with_node_features)
\ngraphs.append(spearman_train_graph_with_node_features)
\ngraphs.append(spearman_test_graph_with_node_features)
\ngraphs.append(spearman_validation_graph_with_node_features)<\/p>\n

In[43]:<\/h1>\n

summary = pd.DataFrame(
\n[(g.number_of_nodes(), g.number_of_edges()) for g in graphs],
\ncolumns=["nodes", "edges"],
\n)
\nsummary.describe().round()<\/p>\n

In[44]:<\/h1>\n

graph_labels = all_stock_nodes<\/h1>\n

In[45]:<\/h1>\n

Generator<\/h1>\n

#generator = FullBatchNodeGenerator(spearman_graph_with_node_features, method = "gcn") # , sparse = False
\n#vars(generator)<\/p>\n

generator = PaddedGraphGenerator( graphs = graphs)<\/p>\n

generator = PaddedGraphGenerator( spearman_graph_with_node_features)<\/h1>\n

In[46]:<\/h1>\n

vars(generator)<\/p>\n

# Train Test Split<\/h1>\n

# Commented out on 2023-04-18<\/h1>\n

train_subjects, test_subjects = model_selection.train_test_split(<\/h1>\n

spearman_graph_node_data<\/h1>\n

)<\/h1>\n

#<\/p>\n

val_subjects, test_subjects_step_2 = model_selection.train_test_split(<\/h1>\n

test_subjects<\/h1>\n

)<\/h1>\n

#<\/p>\n

#, train_size = 500, test_size = None, stratify = test_subjects<\/h1>\n

#<\/p>\n

train_subjects.shape, test_subjects.shape, val_subjects.shape, test_subjects_step_2.shape<\/h1>\n

In[98]:<\/h1>\n","protected":false},"excerpt":{"rendered":"

#!\/usr\/bin\/env python coding: utf-8 # 3rd Model: Deepgraph CNN: Stock Price Prediction using DeepGraphCNN Neural Networks. It includes GCN layers and CNN layers. I have added an MLP at the last layer to predict stock prices. # # Input graphs were created for spearman, Spearman, and Kendal Tau correlations\/coefficients from historical stock prices. Also, another … <\/p>\n

Continue reading<\/a><\/p>\n","protected":false},"author":2,"featured_media":0,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"_jetpack_memberships_contains_paid_content":false,"footnotes":""},"categories":[182],"tags":[],"class_list":["post-76091","post","type-post","status-publish","format-standard","hentry","category---blog","item-wrap"],"jetpack_featured_media_url":"","jetpack_sharing_enabled":true,"jetpack-related-posts":[{"id":78242,"url":"http:\/\/bangla.sitestree.com\/?p=78242","url_meta":{"origin":76091,"position":0},"title":"Statistics for Data Analytics and Machine Learning Projects","author":"Sayed","date":"May 22, 2025","format":false,"excerpt":"\u2022Null Hypothesis \u2022[2] \u2022Paired t-test \u2022Unpaired t-test \u2022Pearson Correlation \u2022One Way: Analysis of variance \u2022Spearman Correlation \u2022Spearman \u2022Kendal Tau Coef \u2022Wilcoxon Sum test \u2022Basic EDA \u2022Mcnaimer\u2019s test \u2022Friedman test \u2022Kruskal-Wallis Test \u2022Two Way Analysis of variance \u2022K-Fold Cross Validation paired t-test \u2022Wilcoxon Signed Rank Test Data Analytics, Machine Learning, Data\u2026","rel":"","context":"In "Analytics and Machine Learning Project Development"","block_context":{"text":"Analytics and Machine Learning Project Development","link":"http:\/\/bangla.sitestree.com\/?cat=1974"},"img":{"alt_text":"","src":"https:\/\/i0.wp.com\/bangla.sitestree.com\/wp-content\/uploads\/2025\/05\/image-37.png?resize=350%2C200","width":350,"height":200,"srcset":"https:\/\/i0.wp.com\/bangla.sitestree.com\/wp-content\/uploads\/2025\/05\/image-37.png?resize=350%2C200 1x, https:\/\/i0.wp.com\/bangla.sitestree.com\/wp-content\/uploads\/2025\/05\/image-37.png?resize=525%2C300 1.5x"},"classes":[]},{"id":76436,"url":"http:\/\/bangla.sitestree.com\/?p=76436","url_meta":{"origin":76091,"position":1},"title":"1. Libraries used for the project: Predict Future Stock Price using Graph Theory, Machine Learning and Deep Learning)","author":"Sayed","date":"December 4, 2024","format":false,"excerpt":"#import libraries import osimport pandas as pdimport math #Import Libraries for Graph, GNN, and GCN import stellargraph as sgfrom stellargraph import StellarGraphfrom stellargraph.layer import DeepGraphCNNfrom stellargraph.mapper import FullBatchNodeGeneratorfrom stellargraph.mapper import PaddedGraphGeneratorfrom stellargraph.layer import GCN #Machine Learnig related library Imports from tensorflow.keras import layers, optimizers, losses, metrics, Modelfrom sklearn import preprocessing,\u2026","rel":"","context":"In "Code: Predict Future Stock Price using Graph Theory, Machine Learning and Deep Learning)"","block_context":{"text":"Code: Predict Future Stock Price using Graph Theory, Machine Learning and Deep Learning)","link":"http:\/\/bangla.sitestree.com\/?cat=1969"},"img":{"alt_text":"","src":"","width":0,"height":0},"classes":[]},{"id":16302,"url":"http:\/\/bangla.sitestree.com\/?p=16302","url_meta":{"origin":76091,"position":2},"title":"Graph Mining: Shared Nearest Neighbors : Clustering : Community Detection","author":"Sayed","date":"October 7, 2019","format":false,"excerpt":"Graph Mining: Shared Nearest Neighbors : Clustering : Community Detection Graph Mining: Shared Nearest Neighbors (SNN): Clustering : Community Detection: Learn by Finding Answers to the Following Questions. Will use SNN sometimes. What is one another name of the algorithm: Shared Nearest Neighbors? What is the purpose of the Algorithm:\u2026","rel":"","context":"In "Graph Mining"","block_context":{"text":"Graph Mining","link":"http:\/\/bangla.sitestree.com\/?cat=1905"},"img":{"alt_text":"","src":"","width":0,"height":0},"classes":[]},{"id":22885,"url":"http:\/\/bangla.sitestree.com\/?p=22885","url_meta":{"origin":76091,"position":3},"title":"Graph Mining: Shared Nearest Neighbors – Community Detection","author":"Sayed","date":"March 21, 2021","format":false,"excerpt":"Resources to Learn From Resources:Jarvis-Patrick Clusteringhttps:\/\/btluke.com\/jpclust.htmlEmpirical Comparison of Algorithms for Network Community Detectionhttps:\/\/cs.stanford.edu\/~jure\/pubs\/communities-www10.pdf Read the resources above to find answers. Shared Nearest Neighbors : Clustering : Community Detection Graph Mining: Shared Nearest Neighbors : Clustering : Community Detection Graph Mining: Shared Nearest Neighbors (SNN): Clustering : Community Detection: Learn by\u2026","rel":"","context":"In "Graph Mining"","block_context":{"text":"Graph Mining","link":"http:\/\/bangla.sitestree.com\/?cat=1905"},"img":{"alt_text":"","src":"","width":0,"height":0},"classes":[]},{"id":22891,"url":"http:\/\/bangla.sitestree.com\/?p=22891","url_meta":{"origin":76091,"position":4},"title":"Graph Mining: Misc. Topics to Learn: Misc. Resources to Learn From","author":"Sayed","date":"March 21, 2021","format":false,"excerpt":"Graph Mining: Misc. Topics to Learn: Misc. Resources to Learn From Influence\/Virus\/Label Propagation Resources to learn fromPage A presentation on Influence\/Virus PropagationURL Big Data Graph Databases Resources to Learn FromPage Graph DatabaseURL Neo4jPage Big Data Graph Processing Resources to Learn FromPage ToolURL Techniques, Tools and Applications of Graph AnalyticURL Graph\u2026","rel":"","context":"In "Graph Mining"","block_context":{"text":"Graph Mining","link":"http:\/\/bangla.sitestree.com\/?cat=1905"},"img":{"alt_text":"","src":"","width":0,"height":0},"classes":[]},{"id":78178,"url":"http:\/\/bangla.sitestree.com\/?p=78178","url_meta":{"origin":76091,"position":5},"title":"How to Report (or Present) the outcome of your Analytics\/ML Project","author":"Sayed","date":"May 18, 2025","format":false,"excerpt":"Reporting and Analysis \u2022Examples \u2022Results section: Page 51: STOCK MARKET PREDICTION USING ENSEMBLE OF GRAPHTHEORY, MACHINE LEARNING AND DEEP LEARNING MODELS \u2022https:\/\/scholarworks.sjsu.edu\/cgi\/viewcontent.cgi?article=1692&context=etd_projects \u2022Check Results and Discussion sections \u2022https:\/\/arxiv.org\/ftp\/arxiv\/papers\/2203\/2203.06848.pdf \u2022A Comparative Study on Forecasting of Retail Sales May be complicated: Learning Context-Aware Classifier for Semantic Segmentation \u2022https:\/\/arxiv.org\/pdf\/2303.11633.pdf \u2022Learning Context-Aware Classifier for\u2026","rel":"","context":"In "Analytics and Machine Learning Project Development"","block_context":{"text":"Analytics and Machine Learning Project Development","link":"http:\/\/bangla.sitestree.com\/?cat=1974"},"img":{"alt_text":"","src":"https:\/\/i0.wp.com\/bangla.sitestree.com\/wp-content\/uploads\/2025\/05\/image-13.png?resize=350%2C200","width":350,"height":200,"srcset":"https:\/\/i0.wp.com\/bangla.sitestree.com\/wp-content\/uploads\/2025\/05\/image-13.png?resize=350%2C200 1x, https:\/\/i0.wp.com\/bangla.sitestree.com\/wp-content\/uploads\/2025\/05\/image-13.png?resize=525%2C300 1.5x"},"classes":[]}],"_links":{"self":[{"href":"http:\/\/bangla.sitestree.com\/index.php?rest_route=\/wp\/v2\/posts\/76091","targetHints":{"allow":["GET"]}}],"collection":[{"href":"http:\/\/bangla.sitestree.com\/index.php?rest_route=\/wp\/v2\/posts"}],"about":[{"href":"http:\/\/bangla.sitestree.com\/index.php?rest_route=\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"http:\/\/bangla.sitestree.com\/index.php?rest_route=\/wp\/v2\/users\/2"}],"replies":[{"embeddable":true,"href":"http:\/\/bangla.sitestree.com\/index.php?rest_route=%2Fwp%2Fv2%2Fcomments&post=76091"}],"version-history":[{"count":0,"href":"http:\/\/bangla.sitestree.com\/index.php?rest_route=\/wp\/v2\/posts\/76091\/revisions"}],"wp:attachment":[{"href":"http:\/\/bangla.sitestree.com\/index.php?rest_route=%2Fwp%2Fv2%2Fmedia&parent=76091"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"http:\/\/bangla.sitestree.com\/index.php?rest_route=%2Fwp%2Fv2%2Fcategories&post=76091"},{"taxonomy":"post_tag","embeddable":true,"href":"http:\/\/bangla.sitestree.com\/index.php?rest_route=%2Fwp%2Fv2%2Ftags&post=76091"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}