Frameworks to Encode User Preferences for Inferring Topic-sensitive Information Networks.

The connection between online users is the key to the success of many important applications, such as viral marketing. In reality, we often easily observe the time when each user in the network receives a message, yet the users’ connections that empower the message diffusion remain hidden. Therefore, given the traces of disseminated messages, recent research has extensively studied approaches to uncover the underlying diffusion network. Since topic related information could assist the network inference, previous methods incorporated either users’ preferences over topics or the topic distributions of cascading messages. However, methods combining both of them may lead to more accurate results, because they consider a more comprehensive range of available information. In this paper, we investigate this possibility by exploring two principled methods: Weighted Topic Cascade (WTC) and Preference-enhanced Topic Cascade (PTC). WTC and PTC formulate the network inference task as non-smooth convex optimization problems and adopt coordinate proximal gradient descent to solve them. Based on synthetic and real datasets, substantial experiments demonstrate that although WTC is better than several previous approaches in most cases, it is less stable than PTC, which constantly outperforms other baselines with an improvement of 4%∼10% in terms of the F-measure of inferred networks.