A new graph neural network-based recommendation model called KGLN, which
leverages Knowledge Graph (KG) information, was developed to enhance the
accuracy and effectiveness of personalized recommendations. This model begins
by using a single-layer neural network to merge individual node features in the
graph. It then adjusts the aggregation weights of neighboring entities by
incorporating influence factors. The model evolves from a single layer to
multiple layers through iteration, enabling entities to access extensive
multi-order associated entity information. The final step involves integrating
features of entities and users to produce a recommendation score. The model's
performance was evaluated by comparing its effects on various aggregation
methods and influence factors. In tests using the MovieLen-1M and Book-Crossing
datasets, KGLN showed an AUC (Area Under the ROC curve) improvement of 0.3% to
5.9% and 1.1% to 8.2%, respectively, over established benchmark methods like
LibFM, DeepFM, Wide&Deep, and RippleNet.

使用知识图谱信息的一种新型图神经网络推荐模型 (KGLN) 被开发出来，以提高个性化推荐的准确性和有效性。该模型通过合并图中的个体节点特征并调整相邻实体的聚合权重，通过迭代使模型从单层发展到多层，并将实体和用户的特征整合在一起产生推荐得分。在对 MovieLen-1M 和 Book-Crossing 数据集进行测试时，KGLN 相对于 LibFM、DeepFM、Wide&Deep 和 RippleNet 等已建立的基准方法，分别显示出了 0.3% 至 5.9% 和 1.1% 至 8.2% 的 AUC (ROC 曲线下面积) 改进。

基于知识图谱的图神经网络推荐模型

Knowledge graph driven recommendation model of graph neural network

Provenance graphs are structured audit logs that describe the history of a
system's execution. Recent studies have explored a variety of techniques to
analyze provenance graphs for automated host intrusion detection, focusing
particularly on advanced persistent threats. Sifting through their design
documents, we identify four common dimensions that drive the development of
provenance-based intrusion detection systems (PIDSes): scope (can PIDSes detect
modern attacks that infiltrate across application boundaries?), attack
agnosticity (can PIDSes detect novel attacks without a priori knowledge of
attack characteristics?), timeliness (can PIDSes efficiently monitor host
systems as they run?), and attack reconstruction (can PIDSes distill attack
activity from large provenance graphs so that sysadmins can easily understand
and quickly respond to system intrusion?). We present KAIROS, the first PIDS
that simultaneously satisfies the desiderata in all four dimensions, whereas
existing approaches sacrifice at least one and struggle to achieve comparable
detection performance.
Kairos leverages a novel graph neural network-based encoder-decoder
architecture that learns the temporal evolution of a provenance graph's
structural changes to quantify the degree of anomalousness for each system
event. Then, based on this fine-grained information, Kairos reconstructs attack
footprints, generating compact summary graphs that accurately describe
malicious activity over a stream of system audit logs. Using state-of-the-art
benchmark datasets, we demonstrate that Kairos outperforms previous approaches.

KAIROS 是一种基于图神经网络的编码器 - 解码器架构，它学习了溯源图的结构变化的时间演变，以量化每个系统事件的异常程度；基于这些细粒度信息，KAIROS 重构攻击的足迹，在系统审计日志流中生成能准确描述恶意活动的简洁摘要图，使用最先进的基准数据集，我们证明了 KAIROS 优于以前的方法。