结合属性信息的二分网络表示学习

现有的网络表示学习算法主要是针对同质网络或异质网络设计的,而忽略了在推荐系统、搜索引擎和问答系统等领域出现的二分网络的特殊特征以及这类网络所携带着的非常丰富的属性信息。为了解决上述问题,提出了一种结合属性信息的二分网络表示学习方法(ABNE)。该方法首先将连边分解成邻居节点间的间接关系集,嵌入显式关系,接着通过余弦相似性引入并定义节点的属性相似度矩阵,并将其作为权重矩阵的一部分指导有偏随机游走,从而嵌入隐式关系和属性信息。最后通过一个联合优化框架得到同时携带网络结构信息和属性信息的节点表示向量。在四个真实公开数据集上进行了推荐任务,并与其他现有方法进行比较,实验结果表明该算法的优越性和合理性。

Attributed Bipartite Network Representation Learning

Existing network embedding models are mostly designed for homogeneous networks or heterogeneous networks,but ignore the special features of bipartite network which arise in recommender systems,search engines,question answering systems and so on.Meanwhile bipartite networks mostly include rich attribute information.To address the above challenging issues,this paper proposes ABNE(attributed bipartite network embedding).Specifically,ABNE first preserves the explicit relations in the bipartite network by decomposing edges into sets of indirect relationships between neighborhood nodes.Then it calculates attribute similarity matrix by cosine similarity and as part of the weight matrix to guide the biased random walk to embed implicit relations and attribute information.Finally,ABNE introduces an optimization framework to obtain the node representation vector which carries both structure information and attribute information.Several tasks have been conducted on four public datasets and compared with other state-of-the-art embedding models.The experimental results show superiority and rationality of ABNE model.