  The potential impact of an academic paper is determined by various factors,
including its popularity and contribution. Existing models usually estimate
original citation counts based on static graphs and fail to differentiate
values from nuanced perspectives. In this study, we propose a novel graph
neural network to Disentangle the Potential impacts of Papers into Diffusion,
Conformity, and Contribution values (called DPPDCC). Given a target paper,
DPPDCC encodes temporal and structural features within the constructed dynamic
heterogeneous graph. Particularly, to capture the knowledge flow, we emphasize
the importance of comparative and co-cited/citing information between papers
and aggregate snapshots evolutionarily. To unravel popularity, we contrast
augmented graphs to extract the essence of diffusion and predict the
accumulated citation binning to model conformity. We further apply orthogonal
constraints to encourage distinct modeling of each perspective and preserve the
inherent value of contribution. To evaluate models' generalization for papers
published at various times, we reformulate the problem by partitioning data
based on specific time points to mirror real-world conditions. Extensive
experimental results on three datasets demonstrate that DPPDCC significantly
outperforms baselines for previously, freshly, and immediately published
papers. Further analyses confirm its robust capabilities. We will make our
datasets and codes publicly available.
