Node Embedding via Word Embedding for Network Community Discovery

EARNING a representation for nodes in a graph, also known as node embedding, has been an important tool for extracting features that can be used in machine learning problems involving graph-structured data [1]-[4]. Perhaps the most widely adopted node embedding is the one based on the eigendecomposition of the adjacency matrix or the graph Laplacian [2], [5], [6]. Recent advances in word embeddings for natural language processing such as [7] has inspired the development of analogous embeddings for nodes in graphs [3], [8]. These so-called "neural" node embeddings have been applied to a number of supervised learning problems such us link prediction and node classification and demonstrated stateof-the-art performance [3], [4], [8]. In contrast to applications to supervised learning problems in graphs, in this work we leverage the neural embedding framework to develop an algorithm for the unsupervised community discovery problem in graphs [9]-[12]. The key idea is straightforward: learn node embeddings such that vectors of similar nodes are close to each other in the latent embedding space. Then, the problem of discovering communities in a graph can be solved by finding clusters in the embedding space. We focus on non-overlapping communities and validate the performance of the new approach through a comprehensive set of experiments on both synthetic and real-world data.