How can we estimate the importance of nodes in a knowledge graph (KG)? A KG is a multi-relational graph that has proven valuable for many tasks including question answering and semantic search. In this paper, we present GENI, a method for tackling the problem of estimating node importance in KGs, which enables several downstream applications such as item recommendation and resource allocation. While a number of approaches have been developed to address this problem for general graphs, they do not fully utilize information available in KGs, or lack flexibility needed to model complex relationship between entities and their importance. To address these limitations, we explore supervised machine learning algorithms. In particular, building upon recent advancement of graph neural networks (GNNs), we develop GENI, a GNN-based method designed to deal with distinctive challenges involved with predicting node importance in KGs. Our method performs an aggregation of importance scores instead of aggregating node embeddings via predicate-aware attention mechanism and flexible centrality adjustment. In our evaluation of GENI and existing methods on predicting node importance in real-world KGs with different characteristics, GENI achieves 5-17% higher NDCG@100 than the state of the art.

Given sparse multi-dimensional data (e.g., (user, movie, time; rating) for movie recommendations), how can we discover latent concepts/relations and predict missing values? Tucker factorization has been widely used to solve such problems with multi-dimensional data, which are modeled as tensors. However, most Tucker factorization algorithms regard and estimate missing entries as zeros, which triggers a highly inaccurate decomposition. Moreover, few methods focusing on an accuracy exhibit limited scalability since they require huge memory and heavy computational costs while updating factor matrices.In this paper, we propose P-TUCKER, a scalable Tucker factorization method for sparse tensors. P-TUCKER performs alternating least squares with a row-wise update rule in a fully parallel way, which significantly reduces memory requirements for updating factor matrices. Furthermore, we offer two variants of P-TUCKER: a caching algorithm P-TUCKER-CACHE and an approximation algorithm P-TUCKER-APPROX, both of which accelerate the update process. Experimental results show that P-TUCKER exhibits 1.7-14.1× speed-up and 1.4-4.8× less error compared to the state-of-the-art. In addition, P-TUCKER scales near linearly with the number of observable entries in a tensor and number of threads. Thanks to P-TUCKER, we successfully discover hidden concepts and relations in a large-scale real-world tensor, while existing methods cannot reveal latent features due to their limited scalability or low accuracy.

Background Acute kidney injury (AKI) is a critical issue in cancer patients because it is not only a morbid complication but also able to interrupt timely diagnostic evaluation or planned optimal treatment. However, the impact of AKI on overall mortality in cancer patients remains unclear. Methods We conducted a retrospective cohort study of 67 986 cancer patients, from 2004 to 2013 to evaluate the relationship between AKI and all‐cause mortality. We used KDIGO AKI definition and grading system. Results During 3.9 ± 3.1 years of follow‐up, 33.8% of the patients experienced AKI at least once. Among AKI events, stage 1, 2, and 3 was 71.0%, 13.8%, and 15.1%, respectively. AKI incidence was highest in hematologic malignancies, followed by urinary tract cancer, and hepatocellular carcinoma. Male sex, older age, underlying diabetes and hypertension, lower serum albumin and plasma hemoglobin, more frequent radio‐contrast exposure, entrance of clinical trials, and receiving chemotherapy were associated with AKI occurrence. AKI development was an independent risk factor for elevated mortality in cancer patients with dose‐responsive manner (Stage 1, hazard ratio [HR] 1.183, 95% confidence interval [CI] 1.145‐1.221, P < 0.001; Stage 2, HR 1.710, 95% CI 1.629‐1.796; Stage 3, HR 2.000, 95% CI 1.910‐2.095; No AKI, reference group) even after adjustment. This tendency was reproduced in various cancer types except thyroid cancer and in various treatment modalities, however, not shown in patients with baseline renal dysfunction. Conclusion AKI was an independent risk factor for all‐cause mortality in overall cancer patients with dose‐responsive manner.

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A connected component in a graph is a set of nodes linked to each other by paths. The problem of finding connected components has been applied to diverse graph analysis tasks such as graph partitioning, graph compression, and pattern recognition. Several distributed algorithms have been proposed to find connected components in enormous graphs. Ironically, the distributed algorithms do not scale enough due to unnecessary data IO & processing, massive intermediate data, numerous rounds of computations, and load balancing issues. In this paper, we propose a fast and scalable distributed algorithm PACC (Partition-Aware Connected Components) for connected component computation based on three key techniques: two-step processing of partitioning & computation, edge filtering, and sketching. PACC considerably shrinks the size of intermediate data, the size of input graph, and the number of rounds without suffering from load balancing issues. PACC performs 2.9 to 10.7 times faster on real-world graphs compared to the state-of-the-art MapReduce and Spark algorithms.

How can we predict the occurrence of acute kidney injury (AKI) in cancer patients based on machine learning with serum creatinine data? Given irregular and heterogeneous clinical data, how can we make the most of it for accurate AKI prediction? AKI is a common and significant complication in cancer patients, and correlates with substantial morbidity and mortality. Since no effective treatment for AKI still exists, it is important to take timely preventive measures. While several approaches have been proposed for predicting AKI, their scope and applicability are limited as they either assume regular data measured over a short hospital stay, or do not fully utilize heterogeneous data. In this paper, we provide an AKI prediction model with a greater applicability, which relaxes the constraints of existing approaches, and fully utilizes irregular and heterogeneous data for learning the model. In a cohort of 21,022 cancer patients who were registered into Korea Central Cancer Registry (KCCR) in Seoul National University Hospital between January 1, 2004 and December 31, 2013, our method achieves 0.7892 precision, 0.7506 recall, and 0.7576 F-measure in predicting whether a patient will develop AKI during the next 14 days.

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