.. _supported_models-Node2vec:

Node2vec
=============

-----------------
Introduction
-----------------

`\[paper\] <https://dl.acm.org/doi/10.1145/2939672.2939754>`_

**Title:** node2vec: Scalable Feature Learning for Networks

**Authors:** Aditya Grover, Jure Leskovec

**Abstract:** Prediction tasks over nodes and edges in networks require careful effort in engineering features used by learning algorithms. Recent research in the broader field of representation learning has led to significant progress in automating prediction by learning the features themselves. However, present feature learning approaches are not expressive enough to capture the diversity of connectivity patterns observed in networks. Here we propose node2vec, an algorithmic framework for learning continuous feature representations for nodes in networks. In node2vec, we learn a mapping of nodes to a low-dimensional space of features that maximizes the likelihood of preserving network neighborhoods of nodes. We define a flexible notion of a node's network neighborhood and design a biased random walk procedure, which efficiently explores diverse neighborhoods. Our algorithm generalizes prior work which is based on rigid notions of network neighborhoods, and we argue that the added flexibility in exploring neighborhoods is the key to learning richer representations. We demonstrate the efficacy of node2vec over existing state-of-the-art techniques on multi-label classification and link prediction in several real-world networks from diverse domains. Taken together, our work represents a new way for efficiently learning state-of-the-art task-independent representations in complex networks.

----------------------
Running with XGCN
----------------------

XGCN provide two implementations of node2vec:

(1) ``Nod2vec``: from pytorch-geometric `Node2vec <https://pytorch-geometric.readthedocs.io/en/latest/generated/torch_geometric.nn.models.Node2Vec.html>`_

(2) ``GensimNode2vec``: from Gensim `Word2vec <https://radimrehurek.com/gensim/models/word2vec.html>`_

Node2vec
-----------------

**Configuration template for Node2vec:**

.. code:: yaml

    # config/Node2vec-config.yaml
    # Dataset/Results root
    data_root: ""
    results_root: ""

    # Trainer configuration
    epochs: 200
    use_validation_for_early_stop: 1  # bool (0 or 1)
    val_freq: 1
    key_score_metric: r100
    convergence_threshold: 20
    val_method: ""
    val_batch_size: 256
    file_val_set: ""

    # Testing configuration
    test_method: ""
    test_batch_size: 256
    file_test_set: ""

    # DataLoader configuration
    train_batch_size: 2048
    num_workers: 6

    # Model configuration
    model: Node2vec
    seed: 1999
    device: cpu
    emb_dim: 64
    emb_lr: 0.005
    walk_length: 12
    num_walks: 8
    context_size: 5
    p: 1.0
    q: 1.0
    num_neg: 4


**Run Node2vec from command line:**

.. code:: bash

    # script/examples/facebook/run_Node2vec.sh
    # set to your own path:
    all_data_root='/home/sxr/code/XGCN_and_data/XGCN_data'
    config_file_root='/home/sxr/code/XGCN_and_data/XGCN_library/config'

    dataset=facebook
    model=Node2vec
    seed=0
    device="cuda:1"

    data_root=$all_data_root/dataset/instance_$dataset
    results_root=$all_data_root/model_output/$dataset/$model/[seed$seed]

    python -m XGCN.main.run_model --seed $seed \
        --config_file $config_file_root/$model-config.yaml \
        --data_root $data_root --results_root $results_root \
        --val_method one_pos_k_neg \
        --file_val_set $data_root/val-one_pos_k_neg.pkl \
        --key_score_metric r20 \
        --test_method multi_pos_whole_graph \
        --file_test_set $data_root/test-multi_pos_whole_graph.pkl \
        --device $device \

GensimNode2vec
-----------------

**Configuration template for GensimNode2vec:**

.. code:: yaml

    # config/GensimNode2vec-config.yaml
    # Dataset/Results root
    data_root: ""
    results_root: ""

    # Trainer configuration
    epochs: 200
    use_validation_for_early_stop: 1  # bool (0 or 1)
    val_freq: 1
    key_score_metric: r100
    convergence_threshold: 20
    val_method: ""
    val_batch_size: 256
    file_val_set: ""

    # Testing configuration
    test_method: ""
    test_batch_size: 256
    file_test_set: ""

    # Model configuration
    model: GensimNode2vec
    seed: 1999

    emb_dim: 64
    emb_lr: 0.01
    num_walks: 16
    walk_length: 16
    p: 1.0
    q: 1.0
    context_size: 5
    num_neg: 5

    num_workers: 6



**Run GensimNode2vec from command line:**

.. code:: bash

    # script/examples/facebook/run_GensimNode2vec.sh
    # set to your own path:
    all_data_root='/home/sxr/code/XGCN_and_data/XGCN_data'
    config_file_root='/home/sxr/code/XGCN_and_data/XGCN_library/config'

    dataset=facebook
    model=GensimNode2vec
    seed=0

    data_root=$all_data_root/dataset/instance_$dataset
    results_root=$all_data_root/model_output/$dataset/$model/[seed$seed]

    python -m XGCN.main.run_model --seed $seed \
        --config_file $config_file_root/$model-config.yaml \
        --data_root $data_root --results_root $results_root \
        --val_method one_pos_k_neg \
        --file_val_set $data_root/val-one_pos_k_neg.pkl \
        --key_score_metric r20 \
        --test_method multi_pos_whole_graph \
        --file_test_set $data_root/test-multi_pos_whole_graph.pkl \