关键词: 社交网络, 邻居结构, 节点属性, 中心度度量, 行为预测

Abstract: Rich knowledge can be acquired by mining the network topology, especially the various sub-structures that each node lies in. Different structures are formed by different potential interactions, which on the other hand indicates different effects between the centric node and its surroundings. In fact, the interaction between the neighbor structures and the mutual influence between the nodes are cause and effect. This paper aims at three most common neighbor structures and proposes DNSI (neighbor structure influence based on deep learning) to analyze the network neighbor structure influence combined with deep learning. By extracting features of the network data in the image format, DNSI can obtain the influence of three neighbor structures. This paper conducts this model on several real-world web data sets to predict node properties, measures category nodes centrality and predicts user behaviors. The experimental results show that this model has superiority in the vast majority of cases.

Key words: social networks, neighbor structure, node properties, centrality measurement, behavior prediction