Distance-Enhanced Graph Neural Network for Link Prediction

We propose a simple yet effective method for link predciton, where the GNN is incorported with a distance, and we can observer performance improvement.

Requirements

Python>=3.6
PyTorch>=1.2
torch-geometric>=1.6.0
ogb>=1.2.3

Step-1: Data Porcessing

python process_data.py --name $DATASET --dir $EDGE_DIR

In this step, the edges of the datasets are stored in $EDGE_DIR to prepare for the next step of extracting information. $DATASET is the data we process, it can be "ogbl-ddi", "ogbl-collab" or "ogbl-ppa". $EDGE_DIR is the place we store the edges.

Step-2: Generate Feature

Compile:

g++ -std=c++11 -o generate_feature generate_feature.cpp random_tree.cpp shortest_path.cpp katz_distance.cpp anchor_distance.cpp common_neighbors.cpp jaccard_coefficient.cpp adamic_adar.cpp resource_allocation.cpp

Generate Feature:

./generate_feature $Distance_NAME $EDGE_DIR $INFO_DIR <$NUM_TREES> <$NUM_ANCHOR_NODES> <$MAX_DISTANCE> <$USE_MIN_ANCHOR> <$EXTRA_RANDOM_EDGES> <$BETA> <$MAX_LENGTH> <$USE_VAL>

In this work, we implement variants of distances

$Distance_NAME is the name of distance.
$EDGE_DIR is the place we store the edges.
$INFO_DIR is the place we store edges' infomation.
$NUM_ANCHOR_NODES is the anchor nodes we selected. Used for anchor-based distance (recommended)
$USE_MIN_ANCHOR is whether use the minimum distance among anchor points.
$EXTRA_RANDOM_EDGES is the number of random edges we added to the graph each time we select a anchor node.
$NUM_TREES is the random tree we generated. Used for tree-based distance.
$MAX_DISTANCE is the default maximum distance.
$BETA is the parameter of Katz Index.
$MAX_LENGTH is the maximum number of steps.
$USE_VAL is whether we use the validation data as the input of test data.

We can generate distances using the following commands:

Random Tree: ./generate_feature random_tree $EDGE_DIR $INFO_DIR $NUM_TREES $USE_VAL
Anchor Distance: ./generate_feature anchor_distance $EDGE_DIR $INFO_DIR $NUM_ANCHOR_NODES $MAX_DISTANCE $USE_MIN_ANCHOR $EXTRA_RANDOM_EDGES $USE_VAL
Shortest Path: ./generate_feature shortest_path $EDGE_DIR $INFO_DIR $MAX_DISTANCE $USE_VAL
Katz Distance: ./generate_feature katz_distance $EDGE_DIR $INFO_DIR $BETA $MAX_LENGTH
Common Neighbors & Jaccard Coefficient & Adamic Adar & Resource Allocation: ./generate_feature common_neighbors/jaccard_coefficient/adamic_adar/resource_allocation $USE_VAL

Step-3: Run Jobs

Baseline: python link_pred.py
Using one kind of distance: python link_pred.py --extra_data_dir $INFO_DIR --extra_data_list $FEATURE_NAME --extra_data_weight $FEATURE_WEIGHT --extra_data_layer $FEATURE_LAYER
Using multiple kinds of distances: python link_pred.py --extra_data_dir $INFO_DIR --extra_data_list $FEATURE_NAME_1 $FEATURE_NAME_2 ... $FEATURE_NAME_N --extra_data_weight $FEATURE_WEIGHT_1 $FEATURE_WEIGHT_2 ... $FEATURE_WEIGHT_N --extra_data_layer $FEATURE_LAYER_1 $FEATURE_LAYER_2 ... $FEATURE_LAYER_N

Our Setting for DDI

Data processing

DATASET='ogbl-ddi'
python process_data.py --name $DATASET --dir ./DDI_edge

EDGE_DIR=DDI_edge
INFO_DIR=DDI_info
NUM_ANCHOR_NODES=1000
MAX_DISTANCE=10
USE_MIN_ANCHOR=
EXTRA_RANDOM_EDGES=
USE_VAL=

./generate_feature anchor_distance $EDGE_DIR $INFO_DIR $NUM_ANCHOR_NODES $MAX_DISTANCE $USE_MIN_ANCHOR $EXTRA_RANDOM_EDGES $USE_VAL

Running our algorithm

L=2
hid=512

python link_pred.py --extra_data_dir './DDI_info'  \
--extra_data_weight 0.1 \
--extra_data_layer 1 \
--dataset ogbl-ddi \
--extra_data_list anchor_distance \
--model GraphSAGE \
--num_layers $L --node_emb $hid --hidden_channels $hid --dropout 0.3 --batch_size 70000 --lr 0.003 --epochs 500 --runs 100 --eval_epoch 50

Results

We repeat the experiments for 30 times. The validation Hits@20 is 82.0566 \pm 2.9849. The test Hits@20 is 82.3877 \pm 4.3709.

lbn187/DLGNN