Abstract
Network embedding (NE) is an important method to learn the representations of a network via a low-dimensional space. Conventional NE models focus on capturing the structural information and semantic information of vertices while neglecting such information for edges. In this work, we propose a novel NE model named BimoNet to capture both the structural and semantic information of edges. BimoNet is composed of two parts; i.e., the bi-mode embedding part and the deep neural network part. For the bi-mode embedding part, the first mode—named the add-mode—is used to express the entity-shared features of edges and the second mode—named the subtract-mode—is employed to represent the entity-specific features of edges. These features actually reflect the semantic information. For the deep neural network part, we firstly regard the edges in a network as nodes, and the vertices as links, which will not change the overall structure of the whole network. Then, we take the nodes’ adjacent matrix as the input of the deep neural network, as it can obtain similar representations for nodes with similar structure. Afterwards, by jointly optimizing the objective function of these two parts, BimoNet could preserve both the semantic and structural information of edges. In experiments, we evaluate BimoNet on three real-world datasets and the task of relation extraction, and BimoNet is demonstrated to outperform state-of-the-art baseline models consistently.
Highlights
Social and information networks are ubiquitous, and contain rich and complex data that record the types and dynamics of human interactions
We chose the datasets from ArnetMiner [34], which were constructed by TransNet [6], in order to compare our model with this recent state-of-the-art model along with the conventional models
We can observe that BimoNet outperformed other baseline models consistently on all datasets in both Filter and
Summary
Social and information networks are ubiquitous, and contain rich and complex data that record the types and dynamics of human interactions. How to mine the information in networks is of high research and application value. Network embedding (NE)—i.e., network representation learning (NRL)—has been proposed to represent the networks so as to realize network analysis, such as link prediction [1], clustering [2], and information retrieval [3]. NE aims to encode the information and features of each vertex into a low-dimensional space; i.e., learn real-valued vector representations for each vertex, so as to reconstruct the network in the learned embedding space. Compared with conventional symbol-based representations, NE could alleviate the issues of computation and sparsity, and manage and represent large-scale networks efficiently and effectively. Most existing NE models only focus on modeling vertices; for example, the classical NE model DeepWalk [4] utilizes random walks to capture the structure of the whole network and CANE [5]
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