Latent Social Characteristics Detection via Information Network Mining
Discovering the latent social characteristics of nodes in an information network has been an important task. The latent merits and characteristics can be represented as a vector, where each dimension may be associated with a special meaning (e.g. topic). These vector representations are essential in understanding the users and the network, and they are applicable to many network-based tasks as well, such as clustering, classification, and link prediction and so on.
In this thesis, I will start from the political ideology estimation problem as a valid example of latent social characteristics. I will cover learning users' latent characteristics (embedding) through (1) bipartite networks between user and other entities; (2) networks with different types of links between users and (3) evolving social networks. Extensive evaluation and case studies are conducted to demonstrate the effectiveness of the estimated representations. Finally, I will investigate the scenario where multiple sets of social characteristics are present in the network, and generalize the methodology into a boarder and more general framework.