提出双分支图模型，同时实现跨类型迁移与关系专精，提升动态异构图建模效果。
THGFM: Dual-Branch Temporal Heterogeneous Graph Fusion Model
- 双路径架构：共享空间与关系分组分别处理跨类型迁移和关系专精
- 在多个学术图数据集上平均性能提升3.25%，最高达12.37%
- 引入旋转时间注意力机制，直接融合相对时间信息，适合大规模动态关系建模
时间异构图天然适用于描述多样节点与关系类型共存且随时间演化的动态系统。现有方法难以兼顾参数高效的跨类型迁移与关系感知的专门化，通常将时间作为附加特征注入注意力核外。本文提出THGFM，一种面向网络规模的时间异构图融合模型，通过统一的双路径架构解决上述问题。THGFM包含两个分支：共享空间时间注意力分支用于高效跨类型迁移，关系类型分组时间注意力分支用于关系感知的专门化；二者通过类型条件非竞争门控求和融合机制整合，为共享与专用分支分配独立的、类型相关的特征门控，允许双向增强或抑制而不产生零和竞争。为进一步直接融入相对时间信息，引入旋转时间注意力机制，在匹配前对查询与键进行半周期时间旋转。THGFM在多个学术图基准上持续超越基线图变换器模型，在六项任务平均性能上提升3.25%，在OAG-CS PV上最高提升12.37%，在PF-L2、PF-L1上分别提升4.87%、1.18%，在OGBN-MAG、HTAG-ArXiv、HTAG-DBLP上分别提升4.24%、3.73%、4.61%。
原文摘要 · Abstract (English)
Temporal heterogeneous graphs offer a natural abstraction for dynamic relational systems in which diverse node and relation types co-exist and evolve over time. Learning on such graphs requires jointly modeling cross-type structural heterogeneity and the temporal dynamics of interactions, yet existing methods still struggle to reconcile parameter-efficient cross-type transfer with relation-aware specialization, and typically inject time only as additive features outside the attention kernel. We propose \textbf{THGFM}, a web-scale temporal heterogeneous graph fusion model that addresses both limitations within a unified dual-path architecture. THGFM couples a \textit{Shared-Space Temporal Attention} branch for parameter-efficient cross-type transfer with a \textit{Relational Type-Partitioned Temporal Attention} branch for relation-aware specialization, and integrates them through \textit{Dual-Path Relational--Shared Fusion}, instantiated with \textit{Type-Conditioned Non-Competitive Gated Sum Fusion}: a adaptive mechanism that assigns independent, type-conditioned feature-wise gates to the shared and specialized branches, allowing both to be amplified or suppressed without zero-sum competition. To directly incorporate relative time into the attention score, THGFM further introduces \textit{Rotary Temporal Attention}, which rotates queries and keys by half-phases of relative time before matching. THGFM consistently outperforms baseline graph transformer models on academic graphs benchmarks, delivering a $+3.25\%$ six-task mean gain, with peak relative gains of $+12.37\%$ on OAG-CS PV, $+4.87\%$ on PF-$L_2$, and $+1.18\%$ on PF-$L_1$, and $+4.24\%$, $+3.73\%$, and $+4.61\%$ on OGBN-MAG, HTAG-ArXiv, and HTAG-DBLP, respectively.
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