Decoupled Smoothing in Probabilistic Soft Logic

Node classification in networks is a common graph mining task. In this paper, we examine how separating identity (a node’s attribute) and preference (the kind of identities to which a node prefers to link) is useful for node classification in social networks. Building upon recent work by Chin et al. (2019), where the separation of identity and preference is accomplished through a technique called “decoupled smoothing”, we show how models that characterize both identity and preference are able to capture the underlying structure in a network, leading to improved performance in node classification tasks. Specifically, we use probabilistic soft logic (PSL) [2], a flexible and declarative statistical reasoning framework, to model identity and preference. We compare our approach with the original decoupled smoothing method and other node classification methods implemented in PSL, and show that our approach outperforms the state-of-the-art decoupled smoothing method as well as the other node classification methods across several evaluation metrics on a real-world Facebook dataset [24]. ACM Reference Format: Yatong Chen, Bryan Tor, Eriq Augustine, and Lise Getoor. 2020. Decoupled Smoothing in Probabilistic Soft Logic. InMLG ’20: International Workshop on Mining and Learning with Graphs, Aug 24, 2020 San Diego, CA, USA. ACM, New York, NY, USA, 7 pages. https://doi.org/10.1145/nnnnnnn.nnnnnnn