Embeddings have become a key paradigm to learn graph represen-tations and facilitate downstream graph analysis tasks.
Many Network Representation Learning (NRL) methods have been proposed to learn vector representations for vertices in a network recently.
One of the central problems in machine learning and pattern recognition is to develop appropriate representations for complex data.
In particular, we develop a novel graph embedding algorithm, High-Order Proximity preserved Embedding (HOPE for short), which is scalable to preserve high-order proximities of large scale graphs and capable of capturing the asymmetric transitivity.
In this paper, we develop methods to “sample” a small realistic graph from a large real network.
In this paper, we first present a comprehensive analysis of the drawbacks of three widely-used random walk based graph sampling algorithms, called re-weighted random walk (RW) algorithm, Metropolis-Hastings random walk (MH) algorithm and maximum-degree random walk (MD) algorithm.
Random walk fits naturally with this problem because, for most online social networks, the only query we can issue through the interface is to retrieve the neighbors of a given node (i. e., no access to the full graph topology).