A wide variety of machine learning tasks such as knowledge base completion, ontology alignment, and multilabel classification can benefit from incorporating into learning differentiable representations of graphs or taxonomies. While vectors in Euclidean space can theoretically represent any graph, much recent work shows that alternatives such as complex, hyperbolic, order, or box embeddings have geometric properties better suited to modeling realworld graphs. Experimentally these gains are seen only in lower dimensions, however, with performance benefits diminishing in higher dimensions. In this work, we introduce a novel variant of box embeddings that uses a learned smoothing parameter to achieve better representational capacity than vector models in low dimensions, while also avoiding performance saturation common to other geometric models in high dimensions. Further, we present theoretical results that prove box embeddings can represent any DAG. We perform rigorous empirical evaluations of vector, hyperbolic, and regionbased geometric representations on several families of synthetic and realworld directed graphs. Analysis of these results exposes correlations between different families of graphs, graph characteristics, model size, and embedding geometry, providing useful insights into the inductive biases of various differentiable graph representations.
This content will become publicly available on October 1, 2023
Modeling Transitivity and Cyclicity in Directed Graphs via Binary Code Box Embeddings
Modeling directed graphs with differentiable representations is a fundamental requirement for performing machine learning on graphstructured data. Geometric embedding models (e.g. hyperbolic, cone, and box embeddings) excel at this task, exhibiting useful inductive biases for directed graphs. However, modeling directed graphs that both contain cycles and some element of transitivity, two properties common in realworld settings, is challenging. Box embeddings, which can be thought of as representing the graph as an intersection over some learned supergraphs, have a natural inductive bias toward modeling transitivity, but (as we prove) cannot model cycles. To this end, we propose binary code box embeddings, where a learned binary code selects a subset of graphs for intersection. We explore several variants, including global binary codes (amounting to a union over intersections) and pervertex binary codes (allowing greater flexibility) as well as methods of regularization. Theoretical and empirical results show that the proposed models not only preserve a useful inductive bias of transitivity but also have sufficient representational capacity to model arbitrary graphs, including graphs with cycles.
 Award ID(s):
 2106391
 Publication Date:
 NSFPAR ID:
 10392230
 Journal Name:
 NeurIPS 2022
 Sponsoring Org:
 National Science Foundation
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