NetCycle+: A Framework for Collective Evolution Inference in Dynamic Heterogeneous Networks

Abstract

Collective inference has attracted considerable attention in the last decade, where the response variables within a group of instances are correlated and should be inferred collectively, instead of independently. Previous works on collective inference mainly focus on exploiting the autocorrelation among instances in a static network during the inference process. There are also approaches on time series prediction, which mainly exploit the autocorrelation within an instance at different time points during the inference process. However, in many real-world applications, the response variables of related instances can co-evolve over time and their evolutions are not following a static correlation across time, but are following an internal life cycle . In this paper, we study the problem of collective evolution inference , where the goal is to predict the values of the response variables for a group of related instances at the end of their life cycles. This problem is extremely important for various applications, e.g., predicting fund-raising results in crowd-funding and predicting gene-expression levels in bioinformatics. This problem is also highly challenging because different instances in the network can co-evolve over time and they can be at different stages of their life cycles and thus have different evolving patterns. Moreover, the instances in collective evolution inference problems are usually connected through heterogeneous information networks (HINs for short), which involve complex relationships among the instances interconnected by multiple types of links. We propose an approach, called NetCycle+ , by incorporating information from both the correlation among related instances and their life cycles. Furthermore, in order to study the deep dependencies between nodes in the network, we extend the graph convolution model into our algorithm. We compared our approach with existing methods of collective inference and time series analysis on two real-world networks. The results demonstrate that our proposed approach can improve the inference performance by considering the autocorrelation through networks and the life cycles of the instances.