Modeling Topic Diffusion in Multi-Relational Bibliographic Information Networks

Information diffusion has been widely studied in networks, aiming to model the spread of information among objects when they are connected with each other. Most of the current research assumes the underlying network is homogeneous, i.e., objects are of the same type and they are connected by links with the same semantic meanings. However, in the real word, objects are connected via different types of relationships, forming multi-relational heterogeneous information networks. In this paper, we propose to model information diffusion in such multi-relational networks, by distinguishing the power in passing information around for different types of relationships. We propose two variations of the linear threshold model for multi-relational networks, by considering the aggregation of information at either the model level or the relation level. In addition, we use real diffusion action logs to learn the parameters in these models, which will benefit diffusion prediction in real networks. We apply our diffusion models in two real bibliographic information networks, DBLP network and APS network, and experimentally demonstrate the effectiveness of our models compared with single-relational diffusion models. Moreover, our models can determine the diffusion power of each relation type, which helps us understand the diffusion process better in the multi-relational bibliographic network scenario.