Interpretable Research Interest Shift Detection with Temporal Heterogeneous Graphs.

Researchers dedicate themselves to research problems they are interested in and often have evolving research interests in their academic careers. The study of research interest shift detection can help to find facts relevant to scientific training paths, scientific funding trends, and knowledge discovery. Existing methods define specific graph structures like author-conference-topic networks, and co-citing networks to detect research interest shift. They either ignore the temporal factor or miss heterogeneous information characterizing academic activities. More importantly, the detection results lack the interpretations of how research interests change over time, thus reducing the model's credibility. To address these issues, we propose a novel interpretable research interest shift detection model with temporal heterogeneous graphs. We first construct temporal heterogeneous graphs to represent the research interests of the target authors. To make the detection interpretable, we design a deep neural network to parameterize the generation process of interpretation on the predicted results in the form of a weighted sub-graph. Additionally, to improve the training process, we propose a semantic-aware negative data sampling strategy to generate non-interesting auxiliary shift graphs as contrastive samples. Extensive experiments demonstrate that our model outperforms the state-of-the-art baselines on two public academic graph datasets and is capable of producing interpretable results.