Python 3 implementation of the biased random walk from node2vec Aditya Grover, Jure Leskovec and Vid Kocijan. node2vec: Scalable Feature Learning for Networks. A. Grover, J. Leskovec. ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD), 2016.
Most of the codes are from eliorc/node2vec.
Change the type of walks attribute from list of str to list of int.
Delete unused class methods, and merge parallel_generate_walks into the code.
import networkx as nx
from random_walks import RandomWalk
karate_g = nx.read_edgelist('./graph/karate.edgelist')
random_walk = RandomWalk(karate_g, walk_length=3, num_walks=10, p=1, q=1, workers=6)
walklist = random_walk.walks
for w in walklist:
print(w)- graph: Input graph
int - walk_length: Number of nodes in each walk (default: 80)
int - num_walks: Number of walks per node (default: 10)
int - p: Return hyper parameter (default: 1)
float - q: Inout parameter (default: 1)
float - weight_key: On weighted graphs, this is the key for the weight attribute (default: 'weight')
str - workers: Number of workers for parallel execution (default: 1)
int - sampling_strategy: Node specific sampling strategies, supports setting node specific 'q', 'p', 'num_walks' and 'walk_length'. Use these keys exactly. If not set, will use the global ones which were passed on the object initialization
- temp_folder: Path to folder with enough space to hold the memory map of self.d_graph (for big graphs); to be passed joblib.Parallel.temp_folder
str