GitHub - HimangiM/Anomaly_Graph_Neural_Net: Anomaly detection algorithm for social networks using Graph Neural Networks by leveraging graph parameteres, between centrality, degree, closeness, on Enron and Twitter datasets

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Anomaly detection algorithm for social networks using Graph Neural Networks by leveraging graph parameteres, between centrality, degree, closeness, on Enron and Twitter datasets

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Name		Name	Last commit message	Last commit date
Latest commit History 1 Commit
1		1
Enron.true		Enron.true
Untitled Document~		Untitled Document~
ann_bet_cen.py		ann_bet_cen.py
ann_bet_cen_closeness.py		ann_bet_cen_closeness.py
ann_bet_cen_degree.py		ann_bet_cen_degree.py
ann_closeness.py		ann_closeness.py
ann_degree.py		ann_degree.py
ann_graph.py		ann_graph.py
ann_twitter.py		ann_twitter.py
ann_twitter.py~		ann_twitter.py~
bet_cen_closeness.py		bet_cen_closeness.py
bet_cen_closeness_enron_data.txt		bet_cen_closeness_enron_data.txt
bet_cen_degree.py		bet_cen_degree.py
bet_cen_degree_enron_data.txt		bet_cen_degree_enron_data.txt
bet_cen_dict.pkl		bet_cen_dict.pkl
bet_cen_enron_data.csv		bet_cen_enron_data.csv
bet_cen_enron_data.txt		bet_cen_enron_data.txt
between_central.py		between_central.py
between_centrality.py		between_centrality.py
closeness_dict.pkl		closeness_dict.pkl
closeness_enron.py		closeness_enron.py
closeness_enron_data.csv		closeness_enron_data.csv
closeness_twitter.py		closeness_twitter.py
cnn_graph.py		cnn_graph.py
degree_closeness_enron.py		degree_closeness_enron.py
degree_enron.py		degree_enron.py
degree_enron_data.csv		degree_enron_data.csv
degree_enron_data.txt		degree_enron_data.txt
degree_list_tup.pkl		degree_list_tup.pkl
degree_twitter.py		degree_twitter.py
enrondatasetfinal.csv		enrondatasetfinal.csv
graph_create.py		graph_create.py
graph_laplacian.py		graph_laplacian.py
higgs-social_network.edgelist		higgs-social_network.edgelist
twitter.graphml		twitter.graphml
twitter_between_central.py		twitter_between_central.py
twitter_nodes.py		twitter_nodes.py

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Anomaly detection algorithm for social networks using Graph Neural Networks by leveraging graph parameteres, between centrality, degree, closeness, on Enron and Twitter datasets

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