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Efficient Dynamic Graph Processing Based on GPU Accelerated Scheduling and Operation Reduction
Sangho Song, Jihyeon Choi, Donghyeon Cha, Hyeonbyeong Lee, Dojin Choi, Jongtae Lim, Kyoungsoo Bok, Jaesoo Yoo
http://doi.org/10.5626/JOK.2024.51.12.1125
Recent research has focused on utilizing GPUs to process large-scale dynamic graphs. However, processing dynamic graphs often leads to redundant data transmission and processing. This paper proposes an efficient scheme for processing large-scale dynamic graphs in memory-constrained GPU environments. The proposed scheme consists of dynamic scheduling and operation reduction methods. The dynamic scheduling method involves partitioning dynamic graph and maximizing GPU processing power by scheduling partitions based on active and potential active vertices. Also, snapshots are utilized to leverage the time-varying characteristics of the graph. The operation reduction method minimizes GPU computation and memory transfer costs by detecting redundant edge and vertex updates in dynamic graphs through snapshots. By avoiding redundant operations on the same edges or vertices, this method improves performance. Through various performance evaluations, the proposed scheme showed 280% and 108% performance improvements on average compared to a static graph processing scheme and a dynamic graph processing scheme, respectively.
Task-Oriented Dialogue System Using a Fusion Module between Knowledge Graphs
Jinyoung Kim, Hyunmook Cha, Youngjoong Ko
http://doi.org/10.5626/JOK.2024.51.10.882
The field of Task-Oriented Dialogue Systems focuses on using natural language processing to assist users in achieving specific tasks through conversation. Recently, transformer-based pre-trained language models have been employed to enhance performances of task-oriented dialogue systems. This paper proposes a response generation model based on Graph Attention Networks (GAT) to integrate external knowledge data into transformer-based language models for more specialized responses in dialogue systems. Additionally, we extend this research to incorporate information from multiple graphs, leveraging information from more than two graphs. We also collected and refined dialogue data based on music domain knowledge base to evaluate the proposed model. The collected dialogue dataset consisted of 2,076 dialogues and 226,823 triples. In experiments, the proposed model showed a performance improvement of 13.83%p in ROUGE-1, 8.26%p in ROUGE-2, and 13.5%p in ROUGE-L compared to the baseline KoBART model on the proposed dialogue dataset.
Generating Relation Descriptions with Large Language Model for Link Prediction
http://doi.org/10.5626/JOK.2024.51.10.908
The Knowledge Graph is a network consisting of entities and the relations between them. It is used for various natural language processing tasks. One specific task related to the Knowledge Graph is Knowledge Graph Completion, which involves reasoning with known facts in the graph and automatically inferring missing links. In order to tackle this task, studies have been conducted on both link prediction and relation prediction. Recently, there has been significant interest in a dual-encoder architecture that utilizes textual information. However, the dataset for link prediction only provides descriptions for entities, not for relations. As a result, the model heavily relies on descriptions for entities. To address this issue, we utilized a large language model called GPT-3.5-turbo to generate relation descriptions. This allows the baseline model to be trained with more comprehensive relation information. Moreover, the relation descriptions generated by our proposed method are expected to improve the performance of other language model-based link prediction models. The evaluation results for link prediction demonstrate that our proposed method outperforms the baseline model on various datasets, including Korean ConceptNet, WN18RR, FB15k-237, and YAGO3-10. Specifically, we observed improvements of 0.34%p, 0.11%p, 0.12%p, and 0.41%p in terms of Mean Reciprocal Rank (MRR), respecitvely.
Understanding Video Semantic Structure with Spatiotemporal Graph Random Walk
Hoyeoung Yun, Minseo Kim, Eun-Sol Kim
http://doi.org/10.5626/JOK.2024.51.9.801
Understanding a long video focuses on finding various semantic units present in the video and interpreting complex relationships among them. Conventional approaches utilize models based on CNNs or transformers to encode contextual information for short clips and then consider temporal relationships among them. However, such approaches struggle to capture complex relationships among smaller semantic units within video clips. In this paper, we present video inputs using a spatiotemporal graph with objects as vertices and relative space-time information between objects as edges, to explicitly express relationships among these semantic units. Additionally, we proposed a novel method to represent major semantic units as compositions of smaller units using high-order relationship information obtained by spatiotemporal random walks on the graph. Through experiments on CATER dataset, which involved complex actions of multiple objects, we demonstrated that our approach exhibited effective semantic unit capturing capabilities.
Photovoltaic Power Forecasting Scheme Based on Graph Neural Networks through Long- and Short-Term Time Pattern Learning
Jaeseung Lee, Sungwoo Park, Jaeuk Moon, Eenjun Hwang
http://doi.org/10.5626/JOK.2024.51.8.690
As the use of solar energy has become increasingly common in recent years, there has been active research in predicting the amount of photovoltaic power generation to improve the efficiency of solar energy. In this context, photovoltaic power forecasting models based on graph neural networks have been presented, going beyond existing deep learning models. These models enhance prediction accuracy by learning the interactions between regions. Specifically, they consider how the amount of photovoltaic power in a specific region is affected by the climate conditions of adjacent regions and the time pattern of photovoltaic power generation. However, existing models mainly rely on a fixed graph structure, making it difficult to capture temporal and spatial interactions. In this paper, we propose a graph neural networks-based photovoltaic power forecasting scheme that takes into account both long-term and short-term time patterns of regional photovoltaic power generation data. We then incorporate these patterns into the learning process to establish correlations between regions. Compared to other graph neural networks-based prediction models, our proposed scheme achieved a performance improvement of up to 7.49% based on the RRSE, demonstrating its superiority.
Improving Prediction of Chronic Hepatitis B Treatment Response Using Molecular Embedding
Jihyeon Song, Soon Sun Kim, Ji Eun Han, Hyo Jung Cho, Jae Youn Cheong, Charmgil Hong
http://doi.org/10.5626/JOK.2024.51.7.627
Chronic hepatitis B patients with no timely treatment are at a high risk of developing complications such as liver cirrhosis and hepatocellular carcinoma (liver cancer). As a result, various antiviral agents for hepatitis B have been developed, and due to the different components of these antiviral agents, there can be variations in treatment responses among patients. Therefore, selecting the appropriate medication that leads to a favorable treatment response is considered crucial. In this study, in addition to the patient's blood test results and electronic medical records indicating drug prescriptions, information about components of the hepatitis B antiviral agents was incorporated for learning. The aim was to enhance the prediction performance of treatment responses one year after chronic hepatitis B patients' treatment. Molecular embedding of the antiviral agents included both fixed molecular embedding and those generated through an end-to-end structure utilizing a graph neural network model. By comparing with the baseline model, drug molecule embedding was confirmed to contribute to improving performance.
Graph Structure Learning-Based Neural Network for ETF Price Movement Prediction
Hyeonsoo Jo, Jin-gee Kim, Taehun Kim, Kijung Shin
http://doi.org/10.5626/JOK.2024.51.5.473
Exchange-Traded Funds (ETFs) are index funds that mirror particular market indices, usually associated with their low risk and expense ratio to individual investors. Various methods have emerged for accurately predicting ETF price movements, and recently, AI-based technologies have been developed. One representative method involves using time-series-based neural networks to predict the price movement of ETFs. This approach effectively incorporates past price information of ETFs, allowing the prediction of their movement. However, it has a limitation as it only utilizes historical information of individual ETFs and does not account for the relationships and interactions between different ETFs. To address this issue, we propose a model that can capture relationships between ETFs. The proposed model uses graph structure learning to infer a graph representing relationships between ETFs. Based on this, a graph neural network predicts the ETF price movement. The proposed model demonstrates superior performance compared to time-series-based deep-learning models that only use individual ETF information.
Prediction of Dehydrogenation Enthalpy Using Graph Isomorphism Network
Kun Young Choi, Woo Hyun Yuk, Jeong Woo Han, Cham Kill Hong
http://doi.org/10.5626/JOK.2024.51.5.406
This paper conducts dehydrogenation enthalpy prediction that could play an important role in selecting optimal liquid organic hydrogen carriers. We employed graph convolutional networks, which produced molecular embeddings for the prediction. Specifically, we adopted Graph Isomorphism Network (GIN) known to be the most expressive graph-based representation learning model. Our proposed approach could build molecular embeddings. Our proposed approach outperformed conventional machine learning solutions and traditional representations based on chemical physics algorithms. In addition, the performance of the proposed model could be improved with small batch sizes and deeper GCN layers using skip connections.
Graph Structure Learning: Reflecting Types of Relationships between Sensors in Multivariate Time Series Anomaly Detection
http://doi.org/10.5626/JOK.2024.51.3.236
Sensors are used to monitor systems in various fields, such as water treatment systems and smart factories. Anomalies in the system can be detected by analyzing multivariate time series consisting of sensor data. To efficiently detect anomalies, information about the relationships between sensors is required, but this information is generally difficult to obtain. To solve this problem, the previous work used sensor data to identify relationships between sensors, which were then represented using a graph structure. However, in this process, the graph structure only reflects the presence of relationships between sensors, not the types of relationships between sensors. In this pap er, we considered the types of relationships between sensors in graph structure learning and analyzed multivariate time series to detect anomalies in the system. Experiments show that improving detection accuracy in graph structure learning for multivariate time series anomaly detection involves taking into account the different kinds of relationships among sensors.
Explainable Artificial Intelligence in Molecular Graph Classification
Yeongyeong Son, Yewon Shin, Sunyoung Kwon
http://doi.org/10.5626/JOK.2024.51.2.157
With the advancement of artificial intelligence (AI), there is a growing need for explainable artificial intelligence (XAI). Recently, Graph neural network-based XAI research has been actively conducted, but it mainly focuses on generic graphs. Due to the distinctive characteristics relying on the chemical properties of molecular graphs, we emphasize the necessity for research to investigate whether existing XAI techniques can provide interpretability in molecular graphs. In this paper, we employ existing XAI techniques to molecular graphs and assess them quantitatively and qualitatively to see their interpretability. Furthermore, we examine the outcomes after standardizing the significance ratio of essential features, highlighting the significance of sparsity as one of the XAI evaluation metrics.
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