提出新型结构通道HAWKEYE，通过深层共现关系提升时序链接预测性能。
HAWKEYE: Seeing One Layer Deeper -- A Cohesion-Aware Structural Channel for Temporal Link Prediction
- 基于2跳紧密桥机制构建新型结构通道，捕捉深层网络关系。
- 在6个数据集上提升测试AP/MRR达0.6至10.8点，最高增益+10.8。
- 适用于稀疏图场景，对非退化图效果显著，适合时序图建模研究者。
当前最先进的时序链接预测（TLP）模型本质上是多通道信息聚合器，整合了交互历史、时间编码与结构通道。前两者已高度优化，而结构通道仍较为粗糙——例如DyGFormer仅用1-2位邻居共现计数。我们首先进行测量：在稀疏时序图中，经典1跳共同邻居信号近乎随机（判别AUC≈0.50），因两节点极少共享直接邻居；真正有判别力的信号存在于更深层——2跳紧密桥，其判别AUC可达0.73–0.98，覆盖二分图与非二分图。受此启发，我们提出HAWKEYE，一种凝聚力感知的结构通道，逐步维护度、k-core、k-truss等凝聚性指标，并生成2跳紧密桥特征。HAWKEYE可作为时序图模型的即插即用结构通道，无需修改主干网络。将其替换到DyGFormer后，在六个多种子验证数据集（uci, enron, USLegis, CanParl, reddit, mooc）上，测试AP/MRR提升0.6至10.8点。在二分图推荐基准tgbl-subreddit上，单次流式训练三种子实验显示，纯结构模型下HAWKEYE使测试MRR从0.103±0.003提升至0.204±0.005（+10.1点）。该流水线可在五分钟内完成6700万边的tgbl-flight处理。我们进一步分析其适用边界：性能增益与图的训练自由2跳判别AUC正相关，且在退化或饱和图上消失。所有代码、数据与图表脚本均已公开。
原文摘要 · Abstract (English)
State-of-the-art temporal-link-prediction (TLP) models are, in essence, multi-channel information aggregators: they combine an interaction-history channel, a time-encoding channel, and a structure channel. The first two have been refined relentlessly; the structure channel remains a crude afterthought -- DyGFormer encodes it as a 1--2-bit neighbour-cooccurrence count. We begin with a measurement: on sparse temporal graphs the classical 1-hop common-neighbour signal is near-random (discriminative AUC $\approx 0.50$), because two nodes almost never share a direct neighbour; the genuinely discriminative signal lies one hop deeper -- the 2-hop cohesive bridge, whose discAUC reaches 0.73--0.98, on both bipartite and non-bipartite graphs. Motivated by this, we propose HAWKEYE, a cohesion-aware structural channel that incrementally maintains the classical k-family of cohesiveness indicators (degree $\to$ k-core $\to$ k-truss) and forms 2-hop cohesive-bridge features. HAWKEYE is a drop-in replacement for a temporal-graph model's native structure channel, with no change to the backbone. Swapping HAWKEYE into DyGFormer improves test AP/MRR over the cooccurrence channel by +0.6 to +10.8 points across six multi-seed-validated datasets (uci, enron, USLegis, CanParl, reddit, mooc). On the bipartite recommendation benchmark tgbl-subreddit, a 3-seed single-pass struct-only ablation shows HAWKEYE nearly doubling the baseline test MRR (0.103$\pm$0.003 $\to$ 0.204$\pm$0.005, +10.1 points across all three seeds); the streaming pipeline scales to the 67M-edge tgbl-flight in five minutes per pass. We further characterise when it helps: the gain tracks a graph's training-free 2-hop discAUC and vanishes on degenerate or saturated graphs -- a predictable boundary. All code, data, and figure-generation scripts are released.
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