Can Classic Graph Autoencoders Rival SOTA? Surprising Optimizations Reveal Their Power

Researchers from Peking University demonstrate that, by applying modern optimization techniques to the decades‑old Graph Autoencoder (GAE), the model can achieve state‑of‑the‑art link‑prediction performance on benchmarks like ogbl‑ppa, while delivering orders‑of‑magnitude speed improvements, challenging the trend toward ever‑more complex GNNs.

Model Optimizationefficiencygraph autoencoder

0 likes · 10 min read

Can Classic Graph Autoencoders Rival SOTA? Surprising Optimizations Reveal Their Power

Alimama Tech

Nov 22, 2023 · Artificial Intelligence

Robust Link Prediction under Bilateral Edge Noise via Robust Graph Information Bottleneck (RGIB)

The paper introduces Robust Graph Information Bottleneck (RGIB), a framework that jointly mitigates bilateral edge noise in link prediction by decoupling topology, label, and representation information, with two variants (RGIB‑SSL and RGIB‑REP) that achieve up to 12.9% AUC gains on benchmarks and have already boosted click‑through‑rate robustness and revenue in Alibaba’s advertising system.

RGIBRobustnessbilateral noise

0 likes · 13 min read

Robust Link Prediction under Bilateral Edge Noise via Robust Graph Information Bottleneck (RGIB)

DataFunTalk

May 8, 2022 · Artificial Intelligence

Automated Knowledge Graph Representation Learning: From Triples to Subgraphs

This talk introduces the background, key directions, and model designs for automated knowledge‑graph representation learning, covering triple‑based, path‑based, and subgraph‑based approaches, the role of AutoML in searching optimal bilinear scoring functions, and future research challenges such as scalability, inductive inference, and domain‑specific applications.

AutoMLEmbeddingKnowledge Graph

0 likes · 20 min read

Automated Knowledge Graph Representation Learning: From Triples to Subgraphs

Alibaba Cloud Developer

Jul 24, 2019 · Artificial Intelligence

Unlocking Better Knowledge Graph Reasoning: The CrossE Model Explained

CrossE introduces an explicit crossover interaction mechanism for knowledge graph embedding, learning both general and interaction-specific representations of entities and relations, which improves link prediction accuracy and provides interpretable explanations, as demonstrated on benchmark datasets WN18, FB15k, and FB15k-237.

EmbeddingInterpretabilityKnowledge Graph

0 likes · 9 min read

Unlocking Better Knowledge Graph Reasoning: The CrossE Model Explained