HIP：基于距离中心性融合与神经常微分方程的模型无关超图影响力预测
HIP: Model-Agnostic Hypergraph Influence Prediction via Distance-Centrality Fusion and Neural ODEs
AI总结：
HIP框架通过融合距离中心性表示、超图神经网络及LSTM与Neural ODEs混合模块，在未知传播模型条件下实现高精度超图影响力预测，并在14个数据集上验证了其优越性和模块化通用性。
摘要：
预测社交网络中的用户影响力是一个关键问题，而超图作为一种普遍的高阶建模方法，为该任务提供了新的视角。然而，由于缺乏显式的级联或感染概率数据，在超图中推断影响力变得尤为困难。为了解决这一问题，我们提出了HIP，一个统一且与模型无关的框架，用于在未知底层传播模型的情况下进行影响力预测。HIP将多维中心性指标与经过时间重解释的距离矩阵相融合，从而在无可观测传播过程的情况下有效表示节点级别的扩散能力。这些表示进一步通过多跳超图神经网络（HNN）进行处理，以捕获复杂的高阶结构依赖关系，同时利用一个结合长短期记忆（LSTM）网络与神经常微分方程（Neural ODEs）的混合模块对时间相关性进行建模。值得注意的是，HIP本质上具有模块化特性：将标准HGNN替换为先进的DPHGNN，以及将LSTM替换为xLSTM，均能获得同样强大的性能，展示了其架构上的通用性和鲁棒性。在14个真实世界超图数据集上的实证评估表明，HIP在预测精度、鲁棒性以及顶级影响力节点识别方面始终优于现有基线方法，且完全不依赖任何扩散轨迹或传播模型的先验知识。这些发现凸显了HIP作为复杂超图环境中影响力预测的通用解决方案的有效性和适应性。
英文摘要：
Predicting user influence in social networks is a critical problem, and hypergraphs, as a prevalent higher-order modeling approach, provide new perspectives for this task. However, the absence of explicit cascade or infection probability data makes it particularly challenging to infer influence in hypergraphs. To address this, we introduce HIP, a unified and model-independent framework for influence prediction without knowing the underlying spreading model. HIP fuses multi-dimensional centrality indicators with a temporally reinterpreted distance matrix to effectively represent node-level diffusion capacity in the absence of observable spreading. These representations are further processed through a multi-hop Hypergraph Neural Network (HNN) to capture complex higher-order structural dependencies, while temporal correlations are modeled using a hybrid module that combines Long Short-Term Memory (LSTM) networks and Neural Ordinary Differential Equations (Neural ODEs). Notably, HIP is inherently modular: substituting the standard HGNN with the advanced DPHGNN, and the LSTM with xLSTM, yields similarly strong performance, showcasing its architectural generality and robustness. Empirical evaluations across 14 real-world hypergraph datasets demonstrate that HIP consistently surpasses existing baselines in prediction accuracy, resilience, and identification of top influencers, all without relying on any diffusion trajectories or prior knowledge of the spreading model. These findings underline HIP's effectiveness and adaptability as a general-purpose solution for influence prediction in complex hypergraph environments.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
