基于边采样的网络表示学习模型

近年来，以微博、微信、Facebook为代表的社交网络不断发展，网络表示学习引起了学术界和工业界的广泛关注。传统的网络表示学习模型利用图矩阵表示的谱特性，由于其效率低下、效果不佳，难以应用到真实网络中。近几年，基于神经网络的表示学习方法因算法效率高、能较好保存网络结构信息，逐渐成为网络表示学习的主流算法。网络中的节点因为不同类型的关系而相互连接，这些关系里隐藏了非常丰富的信息（如兴趣、家人），但所有现存方法都没有区分节点之间边的关系类型。本文提出一个能够编码这种关系信息的无监督网络表示学习模型NEES，首先通过边采样得到能够反映边关系类型信息的边向量，其次利用边向量为图中每个节点学习到一个低维表示。我们分别在几个真实网络数据上进行了多标签分类、边预测等任务，实验结果表明NEES方法能取得超过现存最好算法的优异效果，且其是可规模化的，可以很好地应用于大型网络的表示与计算。

Edge Sampling Based Network Embedding Model

With the development of online social networks such as Weibo, WeChat, and Facebook, network representation learning has aroused widespread research interests in academy and industry. Traditional network embedding models exploit the spectral properties of matrix representations of graphs, which suffer from both computation and performance bottlenecks when applied to real world networks. Recently, a lot of neural network based embedding models are presented in the literature. They are computationally efficient and preserve the network structure information well. The vertices in the network are connected due to various types of relations, which convey rich information. However, such important information are neglected by all existing models. In this paper, we propose NEES, an unsupervised network embedding model to encode the relations. We first obtain the edge vectors by edge sampling to reflect the relation types of the edges. Then we use the edge vectors to learn a low dimension representation for each node in the graph. We conduct extensive experiments on several social networks and one citation network. The results show that our NEES model outperforms the state-of-the-art methods in multi-label classification and link prediction tasks. NEES is also scalable to large-scale networks in the real world.