GraphML News (August 17th) - Spanner Graph, some new papers

🔧 Google announced Spanner Graph - the infinitely scalable graph database (as the vanilla Spanner) with all the bells and whistles GDBMS have in 2024: support both Graph Query Language (GQL, finally standardized by ISO in April after 8 years of work) and SQL, vector search and full-text search, basic graph algorithms at query time.

Otherwise, it’s mid-August and vacation time, so probably no major news for the next few weeks.

Weekend reading:

Training Language Models on the Knowledge Graph: Insights on Hallucinations and Their Detectability from the large DeepMind team - turns out reducing hallucinations when training LLMs on KGs (ie, recalling training triples) requires an order of magnitude more compute than Chinchilla scaling laws. Lots of qualitative results - have a look! Besides, it is one of the accepted papers at COLM - a new conference specifically tailored for LLM research (rip, ACL/EMNLP).

Topological Blind Spots: Understanding and Extending Topological Deep Learning Through the Lens of Expressivity by Yam Eitan et al. feat Haggai Maron - one of the first studies of expressive power of topological (higher-order) MPNNs. Turns out standard models based on simplicial complexes or cellular networks cannot distinguish many common topological patterns like a Möbius strip vs cylinder. The authors then derive provably more powerful scalable multi-cell networks.

Tokenized and Continuous Embedding Compressions of Protein Sequence and Structure by Amy X. Lu et al feat. Pieter Abbeel and Kyunghyun Cho - a deep dive into the latent space of ESMFold which happens to be quite sparse, it can reduced by 128x without losing in prediction performance.