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Drug Toxicity Prediction Using Integrated Graph Neural Networks and Attention-Based Random Walk Algorithm
Jong-Hoon Park, Jae-Woo Chu, Young-Rae Cho
http://doi.org/10.5626/JOK.2025.52.3.234
The traditional drug development process is often burdened by high costs and lengthy timelines, leading to increasing interest in AI-based drug development. In particular, the importance of AI models for preemptively evaluating drug toxicity is being emphasized. In this study, we propose a novel drug toxicity prediction model, named Integrated GNNs and Attention Randon Walk (IG-ARW). The proposed method integrates various Graph Neural Network (GNN) models and uses attention mechanisms to compute random walk transition probabilities, extracting graph features precisely. The model then conducts random walks to extract node features and graph features, ultimately predicting drug toxicity. IG-ARW was evaluated on three different datasets, demonstrating strong performances with AUC scores of 0.8315, 0.8894, and 0.7476, respectively. Notably, the model was proven to be highly effective not only in toxicity prediction, but also in predicting other drug characteristics.
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.
Fast Personalized PageRank Computation on Very Large Graphs
Sungchan Park, Youna Kim, Sang-goo Lee
http://doi.org/10.5626/JOK.2022.49.10.859
Computation of Personalized PageRank (PPR) in graphs is an important function that is widely utilized in myriad application domains such as search, recommendation, and knowledge discovery. As the computation of PPR is an expensive process, a good number of innovative and efficient algorithms for computing PPR have been developed. However, efficient computation of PPR within very large graphs with over millions of nodes is still an open problem. Moreover, previously proposed algorithms cannot handle updates efficiently, thereby severely limiting their capability of handling dynamic graphs. In this paper, we present a fast converging algorithm that guarantees high and controlled precision. We attempted to improve the convergence rate of the traditional Power Iteration approximation methods and fully exact methods. The results revealed that the proposed algorithm is at least 20 times faster than the Power Iteration and outperforms other state-of-the-art algorithms in terms of computation time.
Proposal of a Graph Based Chat Message Analysis Model for Messenger User Verification
http://doi.org/10.5626/JOK.2022.49.9.696
As crimes and accidents through messengers increase, the necessity of verifying messenger users is emerging. In this study, two graph-based messenger user verification models that apply the traditional author verification problem to chat text were proposed. First, the graph random walk model builds an n-gram transition graph with a previous chat message and verifies the user by learning the characteristic of traversing the transition graph with a message whose author is unknown. The results showed an accuracy of 86% in 10,000 chat conversations. Second, the graph volume model verified the user using the characteristic that the size of the transition graph increased over time and achieved an accuracy of 87% in 1,000 chat conversations. When the density of the chat messages was calculated based on the transmission time, both graph models could guarantee more than 80% accuracy when the chat density was 15 or more.
Practically Secure Key Exchange Scheme based on Neural Network
Sooyong Jeong, Dowon Hong, Changho Seo
http://doi.org/10.5626/JOK.2019.46.2.208
Key exchange is one of the major aspects in cryptography. Recently, compared to the existing key exchange schemes, more efficient key exchange schemes have been proposed based on neural network learning. After the first key exchange scheme based on neural network was proposed, various attack models have been suggested in security analysis. Hebbian learning rule is vulnerable to majority attack which is the most powerful attack. Anti Hebbian learning rule is secure against majority attack has a limitation in efficiency, so we can only use key exchange scheme based on random walk learning rule which is more secure and efficient than the others. However, if we use random walk learning rule, the efficiency which is advantage about neural cryptography is reduced than the other learning rules. In this paper we analyze random walk and neural cryptography, and we propose new learning rule which is more efficient than existing random walk learning rule. Also, we theoretically analyze about key exchange scheme which is uses new learning rule and verify the efficiency and security by implementing majority attack model.
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