Transfer Learning for Temporal Link Prediction

Link prediction on graphs has applications spanning from recommender systems to drug discovery. Temporal link prediction (TLP) refers to predicting future links in a temporally evolving graph and adds additional complexity related to the dynamic nature of graphs. State-of-the-art TLP models incorporate memory modules alongside graph neural networks to learn both the temporal mechanisms of incoming nodes and the evolving graph topology. However, memory modules only store information about nodes seen at train time, and hence such models cannot be directly transferred to entirely new graphs at test time and deployment. In this work, we study a new transfer learning task for temporal link prediction, and develop transfer-effective methods for memory-laden models. Specifically, motivated by work showing the informativeness of structural signals for the TLP task, we augment a structural mapping module to the existing TLP model architectures, which learns a mapping from graph structural (topological) features to memory embeddings. Our work paves the way for a memory-free foundation model for TLP.

The Hierarchical Organization of Syntax

Hierarchies are the backbones of complex systems and their analysis allows for a deeper understanding of their structure and how they evolve. We consider languages to be also complex adaptive systems. Hence, we analyzed the hierarchical organization of historical syntactic networks from German that were created from a corpus of texts from the 11th to 17th centuries. We tracked the emergence of syntactic structures in these networks and mapped them to specific communicative needs. We named these emerging structures communicative hierarchies. We hypothesise that the communicative needs of speakers are the organizational force of syntax. We propose that the emergence of these multiple communicative hierarchies is what shapes syntax, and that these hierarchies are the prerequisite to the Zipf's law. The emergence of communicative hierarchies indicates that the objective of language evolution is not only to increase the efficiency of transferring information. Language is also evolving to increase our capacity to communicate more sophisticated abstractions as we advance as a species.