用泊松-伽马模型捕捉动态知识图谱中关系间的演化依赖。
Poisson-Gamma Modeling of Inter-Relational Dependencies in Dynamic Knowledge Graphs
- 基于泊松-伯努利建模多关系时序链接，引入伽马分布潜变量。
- 在稀疏场景下链接预测表现优异，揭示关系演化模式。
- 适合研究动态知识图谱建模与关系演化分析的学者。
动态知识图谱广泛应用于当今人工智能应用中，用于表示分子结构、社交关系和语言信息等。由于知识图谱随时间演变且常存在噪声和不完整，建模其时序与关系依赖对下游任务至关重要。本文提出PGRE（Poisson-Gamma Relational Evolution）模型，用于建模动态知识图谱中的跨关系依赖。该模型通过泊松-伯努利形式表示多关系时序链接，引入伽马分布潜变量以捕捉实体因子关联及由共享潜社区介导的跨关系依赖。进一步采用伽马马尔可夫过程建模潜变量的时间演化，实现关系动态的合理刻画。在基准数据集上的实验表明，PGRE在链接预测任务中表现具有竞争力，尤其在稀疏设置下；同时揭示了动态知识图谱中具有意义的关系演化模式。
原文摘要 · Abstract (English)
Dynamic knowledge graphs are ubiquitous in today's AI applications, as we represent molecular structures, social relationships, and language information using these graph models. As knowledge graphs evolve over time and are often noisy and incomplete, modeling their temporal and relational dependencies becomes crucial for downstream tasks. To address these challenges, this paper proposes PGRE (Poisson-Gamma Relational Evolution), a probabilistic model for modeling inter-relational dependencies in dynamic knowledge graphs. PGRE represents multi-relational temporal links via a Poisson-Bernoulli formulation. It introduces Gamma-distributed latent variables to capture entity-factor associations and cross-relation dependencies mediated by shared latent communities. A Gamma Markov process further models the temporal evolution of these latent variables, enabling principled characterization of relational dynamics. Experiments on benchmark datasets show that PGRE achieves competitive performance in link prediction, particularly in sparse settings, while revealing meaningful relational evolution patterns in dynamic knowledge graphs.
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