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.
Embeddings have become a key paradigm to learn graph represen-tations and facilitate downstream graph analysis tasks.
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.
Our goal in this paper is to obtain a representative (unbiased) sample of Facebook users by crawling its social graph.
In this paper, we develop methods to “sample” a small realistic graph from a large real network.
While data mining in chemoinformatics studied graph data with dozens of nodes, systems biology and the Internet are now generating graph data with thousands and millions of nodes.