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.

本研究提出了一种新颖的图神经网络模型 DPPDCC，用于解读学术论文的潜在影响，并区分扩散、一致性和贡献价值。通过对构建的动态异构图中的时态和结构特征进行编码，捕捉知识流动的重要性、比较性和引证 / 被引信息，并利用扩展图对扩散进行本质性提取、预测累积引用情况进行一致性建模。通过施加正交约束以促进每个角度的不同建模并保留贡献的内在价值，我们对基于特定时间点划分数据以模拟真实条件的问题进行了重新定义，验证了 DPPDCC 模型对于先前发表、最新发表和即时发表的论文在不同时期的泛化能力，并进行了进一步的分析以验证其鲁棒性。我们将公开提供我们的数据集和代码。

解析论文对传播、一致性和贡献价值的潜在影响

Disentangling the Potential Impacts of Papers into Diffusion,  Conformity, and Contribution Values

A zoo of deep nets is available these days for almost any given task, and it
is increasingly unclear which net to start with when addressing a new task, or
which net to use as an initialization for fine-tuning a new model. To address
this issue, in this paper, we develop knowledge flow which moves 'knowledge'
from multiple deep nets, referred to as teachers, to a new deep net model,
called the student. The structure of the teachers and the student can differ
arbitrarily and they can be trained on entirely different tasks with different
output spaces too. Upon training with knowledge flow the student is independent
of the teachers. We demonstrate our approach on a variety of supervised and
reinforcement learning tasks, outperforming fine-tuning and other 'knowledge
exchange' methods.

通过知识流的方式将多个深度网络（教师）的知识传递给新的深度网络模型（学生），解决了在新任务中选择哪个网络或为微调新模型选择哪个网络的初始化的问题，并在监督和强化学习任务中提供比微调和其他知识交流方法更好的表现。