node2vec randomwalk

Introduction

Node2vec is an algorithm proposed by Aditya Grover published in KDD2016, which is an algorithm framework for learning the feature representation of network nodes. The algorithm proposes a biased random walk process to obtain different neighbor nodes. By using two strategies, depth-first search (DFS) and width-first search (BFS), the random walk generates a set of neighbors of the nodes.

Parameters

use --help param to view detailed help information.

Input Format

Input files should be formatted as follows:

Each line of the input file requires the following format: <src>,<dst> or <src>,<dst>,<weight>, which represents the head and tail of an edge, <src> and <dst> is the id number of uint32_t，weight of type float is the weight of the edge.

Input example (Following numbers are synthetic and are for demonstration purpose only.)：

123,856
856,123

Output Format

Output files are formatted as follows：

For each node, the output is a multi-line text file, each line is a sequence of nodes separated by spaces. The format of each line: <nod1> <nod2> <nod3> <nod4> …

Output example (Following numbers are synthetic and are for demonstration purpose only.)：

1 2 3 4
1 3 8 2

Code

https://github.com/Tencent/plato/blob/master/example/node2vec_randomwalk.cc