Search to Pass Messages for Temporal Knowledge Graph Completion (2022)

Abstract: Completing missing facts is a fundamental task for temporal knowledge graphs (TKGs).Recently, graph neural network (GNN) based methods, which can simultaneously explore topological and temporal information, have become the state-of-the-art (SOTA) to complete TKGs. However, these studies are based on hand-designed architectures and fail to explore the diverse topological and temporal properties of TKG.To address this issue, we propose to use neural architecture search (NAS) to design data-specific message passing architecture for TKG completion.In particular, we develop a generalized framework to explore topological and temporal information in TKGs.Based on this framework, we design an expressive search space to fully capture various properties of different TKGs. Meanwhile, we adopt a search algorithm, which trains a supernet structure by sampling single path for efficient search with less cost.We further conduct extensive experiments on three benchmark datasets. The results show that the searched architectures by our method achieve the SOTA performances.Besides, the searched models can also implicitly reveal diverse properties in different TKGs.Our code is released in https://github.com/striderdu/SPA.

## introduction a temporal knowledge graph (tkg) @xcite is a graph-structural data with many timesensitive relational facts. the facts can be formed as qudaruples (subject entity, relationship, object entity, timestamp), denoted as (s, r, o, t), e.g., @xcite . tkgs are used extensively in various applications that require the assistance of temporal knowledge such as temporal question answering @xcite , recommendation systems @xcite and mobility prediction @xcite . ## the proposed method as mentioned in the introduction, the gnn-based method for tkgc should be data-specific. generally, tkgs contain both topological and temporal information. thus, to design a proper model, we first define a framework which can model topological patterns and temporal contexts jointly. then, we introduce our novel search space. finally, we describe our search objective and search algorithm. ## conclusion in this paper, we propose a novel method spa to automatically design data-specific architectures for tkgc task. we define a novel and expressive search space, in which different combinations of operations can capture various patterns of different tkgs. to enable efficient search on top of the search space, we adopt a flexible and effective search algorithm, which trains a simplified supernet in that each architecture is a single path, thus greatly reducing the gpu memory cost. to demonstrate the effectiveness of spa for tkgc, we conduct extensive experiments on three datasets. the experimental results show that spa can search sota data-specific architectures for tkgc. for future work, we will explore more advanced nas approaches to further improve the search efficiency of spa. besides, a promising direction is to explore how to efficiently search network architectures and hyper-parameters simultaneously.