LINE:Large-scale Information Network Embedding

1.运用场景

    which is suitable for arbitrary types of information networks:undirected,directed,and/or weighted。

2.创新点

    which suits arbitrary types of information networks and easily scales to millions of nodes.It has a carefully designed objective function that preserves both the first-order and second-order proximities；
    propose an edge-sampling algorithm for optimizing the objective.The algorithm tackles the limitation of the classical stochastic gradient decent and improves the effectiveness and efficiency of the inference。

3.算法原理

3.1 网络框架

3.2 LINE

    LINE论文

4.算法理解

    同时考虑first-order proximity and second-order proximity。