Sang-Wook Kim

Yeon-Chang Lee, JaeHyun Lee, Michiharu Yamashita et al. · gatech

The problem of career trajectory prediction (CTP) aims to predict one's future employer or job position. While several CTP methods have been developed for this problem, we posit that none of these methods (1) jointly considers the mutual ternary dependency between three key units (i.e., user, position, and company) of a career and (2) captures the characteristic shifts of key units in career over time, leading to an inaccurate understanding of the job movement patterns in the labor market. To address the above challenges, we propose a novel solution, named as CAPER, that solves the challenges via sophisticated temporal knowledge graph (TKG) modeling. It enables the utilization of a graph-structured knowledge base with rich expressiveness, effectively preserving the changes in job movement patterns. Furthermore, we devise an extrapolated career reasoning task on TKG for a realistic evaluation. The experiments on a real-world career trajectory dataset demonstrate that CAPER consistently and significantly outperforms four baselines, two recent TKG reasoning methods, and five state-of-the-art CTP methods in predicting one's future companies and positions--i.e., on average, yielding 6.80% and 34.58% more accurate predictions, respectively. The codebase of CAPER is available at https://github.com/Bigdasgit/CAPER.

Myung-Hwan Jang, Jeong-Min Park, Yunyong Ko et al.

Graph neural networks (GNNs) have achieved breakthroughs in various real-world downstream tasks due to their powerful expressiveness. As the scale of real-world graphs has been continuously growing, \textit{a storage-based approach to GNN training} has been studied, which leverages external storage (e.g., NVMe SSDs) to handle such web-scale graphs on a single machine. Although such storage-based GNN training methods have shown promising potential in large-scale GNN training, we observed that they suffer from a severe bottleneck in data preparation since they overlook a critical challenge: \textit{how to handle a large number of small storage I/Os}. To address the challenge, in this paper, we propose a novel storage-based GNN training framework, named \textsf{AGNES}, that employs a method of \textit{block-wise storage I/O processing} to fully utilize the I/O bandwidth of high-performance storage devices. Moreover, to further enhance the efficiency of each storage I/O, \textsf{AGNES} employs a simple yet effective strategy, \textit{hyperbatch-based processing} based on the characteristics of real-world graphs. Comprehensive experiments on five real-world graphs reveal that \textsf{AGNES} consistently outperforms four state-of-the-art methods, by up to 4.1$\times$ faster than the best competitor. Our code is available at https://github.com/Bigdasgit/agnes-kdd26.