Learning Graph Distances with Message PassingNeural Networks

Siamese Neural message passing for graph retrieval implementation.

Pau Riba, Andreas Fischer, Josep Lladós and Alicia Fornes, Learning Graph Distances with Message PassingNeural Networks, ICPR2018

Methodology.

Graph Distance

Compute the distance using raw graphs without training:

$ python no_train_hausdorff.py

Learn Representation

Trains a classifier. In test time discards the readout and uses a graph distance.

$ python train_learn_representation.py

Siamese NMP Network

Siamese network. Maps the graph in an Euclidean space and computes the distance.

$ python train_siamese_net.py

Siamese NMP Distance Network

Siamese network. Enrich the graph with node features and computes a Hausdorff Distance

    $ python train_siamese_distance.py -h
    usage: train_siamese_distance.py [-h] [--nlayers NLAYERS]
                                     [--distance {Hd,SoftHd}]
                                     [--representation {adj,feat}]
                                     [--normalization] [--hidden_size HIDDEN_SIZE]
                                     [--write WRITE] [--epochs EPOCHS]
                                     [--batch_size BATCH_SIZE]
                                     [--learning_rate LEARNING_RATE]
                                     [--momentum MOMENTUM] [--decay DECAY]
                                     [--schedule SCHEDULE [SCHEDULE ...]]
                                     [--gamma GAMMA] [--save SAVE] [--load LOAD]
                                     [--test] [--ngpu NGPU] [--prefetch PREFETCH]
                                     [--log LOG] [--log-interval N]
                                     data_path
                                     {letters,histograph,histographretrieval}

Test a trained models:

Letters

    $ python train_siamese_distance.py $DATA_PATH letters -t -l ./trained_models/LETTERS/LOW/feat_SoftHd_l3_h64.pth --distance SoftHd -b 128 --hidden_size 64 --nlayers 3 --representation feat
    Test distance:
        * 1-NN; Average Acc 97.867; Avg Time x Batch 0.480
        * 3-NN; Average Acc 98.000; Avg Time x Batch 0.480
        * 5-NN; Average Acc 98.267; Avg Time x Batch 0.480

    $ python train_siamese_distance.py $DATA_PATH letters -t -l ./trained_models/LETTERS/MED/feat_SoftHd_l3_h64.pth --distance SoftHd -b 128 --hidden_size 64 --nlayers 3 --representation feat
    Test distance:
        * 1-NN; Average Acc 88.533; Avg Time x Batch 0.536
        * 3-NN; Average Acc 88.000; Avg Time x Batch 0.536
        * 5-NN; Average Acc 89.200; Avg Time x Batch 0.536

HIGH

    $ python train_siamese_distance.py $DATA_PATH letters -t -l ./trained_models/LETTERS/HIGH/feat_SoftHd_l3_h64.pth --distance SoftHd -b 128 --hidden_size 64 --nlayers 3 --representation feat
    Test distance:
        * 1-NN; Average Acc 79.200; Avg Time x Batch 0.529
        * 3-NN; Average Acc 82.533; Avg Time x Batch 0.529
        * 5-NN; Average Acc 82.533; Avg Time x Batch 0.529

Histograph

Keypoints

    $ python train_siamese_distance.py $DATA_PATH histograph -t -l ./trained_models/HistoGraph/01_Keypoint/feat_SoftHd_l3_h64.pth --distance SoftHd -b 128 --hidden_size 64 --nlayers 3 --representation feat
    Test distance:
        * 1-NN; Average Acc 87.413; Avg Time x Batch 3.246
        * 3-NN; Average Acc 87.413; Avg Time x Batch 3.246
        * 5-NN; Average Acc 83.217; Avg Time x Batch 3.246

Projection

    $ python train_siamese_distance.py $DATA_PATH histograph -t -l ./trained_models/HistoGraph/05_Progection/feat_SoftHd_l3_h64.pth --distance SoftHd -b 128 --hidden_size 64 --nlayers 3 --representation feat
    Test distance:
        * 1-NN; Average Acc 79.021; Avg Time x Batch 1.979
        * 3-NN; Average Acc 77.622; Avg Time x Batch 1.979
        * 5-NN; Average Acc 70.629; Avg Time x Batch 1.979