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evalne_config.ini
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evalne_config.ini
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[SETUP]
output = ./output
# possible datasets: karate can_96 netscience powergrid facebook twitter_gephi
datasets = karate
# AROPE AROPE_tSNE CNE CNE_DEGREE CNE_tSNE DEEPWALK DEEPWALK_tSNE FR FR_RTX GCN_AE GCN_AE_tSNE DRGRAPH
methods = AROPE AROPE_tSNE CNE CNE_DEGREE CNE_tSNE DEEPWALK DEEPWALK_tSNE FR FR_RTX GCN_AE GCN_AE_tSNE DRGRAPH
repetitions = 1
dimensions = 2
# 'tn', 'fp', 'fn', 'tp', 'auroc', 'precision', 'recall', 'fallout', 'miss', 'accuracy', 'f_score', 'eval_time' or 'edge_embed_method'
metrics = auroc precision recall tn fp fn tp accuracy eval_time edge_embed_method
edge_embed_methods = hadamard weighted_l1 weighted_l2 average
# e.g. random_prediction common_neighbours jaccard_coefficient
baselines =
# To use the same precomputed train/test splits for every method
#load_traintest_split = output/evalne_splits/
# Datasets
[karate]
file = data/karate.txt
delimiter = ','
[can_96]
file = data/can_96.txt
delimiter = ','
[netscience]
file = data/netscience.txt
delimiter = ','
[facebook]
file = data/facebook.txt
delimiter = ','
[powergrid]
file = data/powergrid.txt
delimiter = ','
[twitter_gephi]
file = data/twitter_gephi.txt
delimiter = ','
# Embedding methods
[AROPE]
command = methods/arope_venv/bin/python methods/arope_main.py --inputgraph {} --output None --tr_e {} --te_e {} --tr_pred {} --te_pred {} --dimension {}
parameters = "--order 3 --weights [1,0.1,0.01]"
type = e2e
[AROPE_tSNE]
command = methods/arope_venv/bin/python methods/arope_main.py --inputgraph {} --output {} --dimension {}
parameters = "--order 3 --weights [1,0.1,0.01]"
type = ne
use_tsne = True
emb_dimension = 10
[CNE]
command = python methods/cne/src/main.py --inputgraph {} --tr_e {} --te_e {} --tr_pred {} --te_pred {} --dimension {} --output None --delimiter ','
parameters = "--learning_rate 0.05 --prior uniform --epochs 1000"
type = e2e
[CNE_tSNE]
command = python methods/cne/src/main.py --inputgraph {} --output {} --dimension {} --delimiter ','
parameters = "--learning_rate 0.05 --prior uniform --ftol 1e-3 --epochs 1000"
type = ne
use_tsne = True
emb_dimension = 16
[CNE_DEGREE]
command = python methods/cne/src/main.py --output None --delimiter ',' --inputgraph {} --tr_e {} --te_e {} --tr_pred {} --te_pred {} --dimension {}
parameters = "--learning_rate 0.05 --prior degree --pred_prior uniform --epochs 1000"
type = e2e
[DEEPWALK]
command = methods/deepwalk_venv/bin/python methods/deepwalk_main.py --inputgraph {} --output {} --dimension {} --delimiter ','
parameters = "--workers 4"
type = ne
[DEEPWALK_tSNE]
command = methods/deepwalk_venv/bin/python methods/deepwalk_main.py --inputgraph {} --output {} --dimension {} --delimiter ','
parameters = "--workers 4 "
type = ne
use_tsne = True
emb_dimension = 128
[FR]
command = python methods/fruchterman_reingold.py --inputgraph {} --output {} --dimension {} --delimiter ','
parameters = "--mode networkx --epochs 1000"
type = ne
[FR_RTX]
command = python methods/fruchterman_reingold.py --exec methods/frrtx/build/gd --inputgraph {} --output {} --dimension {} --delimiter ','
parameters = "--mode rtx --epochs 10000"
type = ne
[GCN_AE]
command = methods/gae_venv/bin/python methods/gae_main.py --inputgraph {} --output None --tr_e {} --te_e {} --tr_pred {} --te_pred {} --dimension {}
parameters = ""
type = e2e
[GCN_AE_tSNE]
command = methods/gae_venv/bin/python methods/gae_main.py --inputgraph {} --output {} --dimension {}
parameters = ""
type = ne
use_tsne = True
emb_dimension = 16
[DRGRAPH]
command = python methods/drgraph.py --exec methods/drgraph/Vis --inputgraph {} --output {} --dimension {}
parameters = "--gamma 0.1 --neg_samples 5 --epochs 400 --A 2 --B 1"
type = ne