@inproceedings{Daor19,author={Artem Lutov and Dingqi Yang and Philippe Cudr{\'e}-Mauroux},title={Bridging the Gap between Community and Node Representations: Graph Embedding via Community Detection},year={2019},keywords={parameter-free graph embedding, unsupervisedlearning of network representation, automatic feature extraction,interpretable compact embeddings, scalable graph embedding},}

Deployment

$ ./install_reqs.sh$ ./build.sh

Usage Examples:

$time python3 scoring_classif.py --embeddings embeds/blog.nvc -m cosine -o res/blog.reseval -s liblinear --num-shuffles 3 --network graphs/blog.mat

Options

$ python3 scoring_classif.py -husage: scoring_classif.py [-h] [-w] [--no-dissim] [--root-dims]                          [--dim-vmin DIM_VMIN] [-m METRIC] [-b] [-o OUTPUT]                          [--num-shuffles NUM_SHUFFLES] [-p] [--no-cython]                          {eval,gram,test} ...Network embedding evaluation using multi-lable classification.optional arguments:  -h, --help            show this help message and exit  -w, --weighted-dims   Apply dimension weights if specified (applicable only                        for the NVC format). (default: False)  --no-dissim           Omit dissimilarity weighting (if weights are specified                        at all). (default: False)  --root-dims           Use only root (top) level dimensions (clusers), actual                        only for the NVC format. (default: False)  --dim-vmin DIM_VMIN   Minimal dimension value to be processed before the                        weighting, [0, 1). (default: 0)  -m METRIC, --metric METRIC                        Applied metric for the similarity matrics                        construction: cosine, jaccard, hamming. (default:                        cosine)  -b, --binarize        Binarize the embedding minimizing the Mean Square                        Error. NOTE: the median binarizaion is performed if                        the hamming metric is specified with this flag.                        (default: False)  -o OUTPUT, --output OUTPUT                        A file name for the results. Default: ./<embeds>.res                        or ./gtam_<embeds>.mat. (default: None)  --num-shuffles NUM_SHUFFLES                        Number of shuffles of the embedding matrix, >= 1.                        (default: 5)  -p, --profile         Profile the application execution. (default: False)  --no-cython           Disable optimized routines from the Cython libs.                        (default: False)Embedding processing modes:  {eval,gram,test}    eval                Evaluate embedding.    gram                Produce Gram (network nodes similarity) matrix.    test                Run doc tests for all modules including                        "similarities".

$ python3 scoring_classif.py eval -husage: scoring_classif.py eval [-h] -e EMBEDDING -n NETWORK                               [--adj-matrix-name ADJ_MATRIX_NAME]                               [--label-matrix-name LABEL_MATRIX_NAME]                               [-s SOLVER] [-k KERNEL] [--balance-classes]                               [--all] [--num-shuffles NUM_SHUFFLES]                               [--accuracy-detailed ACCURACY_DETAILED]optional arguments:  -h, --help            show this help message and exit  -e EMBEDDING, --embedding EMBEDDING                        File name of the embedding in .mat, .nvc or .csv/.ssv                        (text) format.  -n NETWORK, --network NETWORK                        An input network (graph): a .mat file containing the                        adjacency matrix and node labels.  --adj-matrix-name ADJ_MATRIX_NAME                        Variable name of the adjacency matrix inside the                        network .mat file.  --label-matrix-name LABEL_MATRIX_NAME                        Variable name of the labels matrix inside the network                        .mat file.  -s SOLVER, --solver SOLVER                        Linear Regression solver: liblinear (fastest), lbfgs                        (less accurate, slower, parallel). ATTENTION: has                        priority over the SVM kernel.  -k KERNEL, --kernel KERNEL                        SVM kernel: precomputed (fast but requires                        gram/similarity matrix), rbf (accurate, slow), linear                        (slow).  --balance-classes     Balance (weight) the grouund-truth classes by their                        size.  --all                 The embedding is evaluated on all training percents                        from 10 to 90 when this flag is set to true. By                        default, only training percents of 0.3, 0.5, 0.7 are                        used.  --num-shuffles NUM_SHUFFLES                        Number of shuffles of the embedding matrix, >= 1.  --accuracy-detailed ACCURACY_DETAILED                        Output also detailed accuracy evalaution results to                        ./acr_<evalres>.mat.

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