Directed Graph Auto-Encoders

Κόλλιας, Γεώργιος; Kalantzis, Vasileios; Idé, Tsuyoshi; Lozano, Aurélie; Abe, Naoki

doi:10.1609/aaai.v36i7.20682

Cited by 10 publications

(3 citation statements)

References 16 publications

(29 reference statements)

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“…Expanding upon this foundational framework, methods like CoLA [23] and LogLG [13] introduce novel indicators, such as the similarity between positive and negative sample pairs, to identify anomalies. Moreover, based on various graph autoencoders [16,20], methods like AdONE [1] and AnomalyDAE [8] compute the reconstruction errors of nodes, thereby deriving anomaly scores.…”

Section: Detection Based On Graphsmentioning

confidence: 99%

PhoGAD: Graph-based Anomaly Behavior Detection with Persistent Homology Optimization

Yuan,

Zhou,

Chen

et al. 2024

Proceedings of the 17th ACM International Conference on Web Search and Data Mining

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A multitude of toxic online behaviors, ranging from network attacks to anonymous traffic and spam, have severely disrupted the smooth operation of networks. Due to the inherent sender-receiver nature of network behaviors, graph-based frameworks are commonly used for detecting anomalous behaviors. However, in real-world scenarios, the boundary between normal and anomalous behaviors tends to be ambiguous. The local heterophily of graphs interferes with the detection, and existing methods based on nodes or edges introduce unwanted noise into representation results, thereby impacting the effectiveness of detection. To address these issues, we propose PhoGAD, a graph-based anomaly detection framework. PhoGAD leverages persistent homology optimization to clarify behavioral boundaries. Building upon this, the weights of adjacent edges are designed to mitigate the effects of local heterophily. Subsequently, to tackle the noise problem, we conduct a formal analysis and propose a disentangled representation-based explicit embedding method, ultimately achieving anomaly behavior detection. Experiments on intrusion, traffic, and spam datasets verify that PhoGAD has surpassed the performance of state-of-the-art (SOTA) frameworks in detection efficacy. Notably, PhoGAD demonstrates robust detection even with diminished anomaly proportions, highlighting its applicability to real-world scenarios. The analysis of persistent homology demonstrates its effectiveness in capturing the topological structure formed by normal edge features. Additionally, ablation experiments

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Section: Detection Based On Graphsmentioning

confidence: 99%

PhoGAD: Graph-based Anomaly Behavior Detection with Persistent Homology Optimization

Yuan,

Zhou,

Chen

et al. 2024

Proceedings of the 17th ACM International Conference on Web Search and Data Mining

View full text Add to dashboard Cite

show abstract

“…The graph structure provides the node values and the relationships of nodes, so GNN can capture more information than regular data. Owing to this merit, graph CNN (GCN) [58,59], graph recurrent neural network [60], graph auto-encoder [61] and so on have been developed. Among them, GCN is the most common and effective method in fault diagnosis, and a twolayer GCN is shown in figure 9.…”

Section: Graph Neural Network (Gnn)mentioning

confidence: 99%

A review on deep learning in planetary gearbox health state recognition: methods, applications, and dataset publication

Liu,

Cui,

Cheng

2023

Meas. Sci. Technol.

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Planetary gearboxes have various merits in mechanical transmission, but their complex structure and intricate operation modes bring large challenges in terms of fault diagnosis. Deep learning has attracted increasing attention in intelligent fault diagnosis and has been successfully adopted for planetary gearbox fault diagnosis, avoiding the difficulty in manually analyzing complex fault features with signal processing methods. This paper presents a comprehensive review of deep learning-based planetary gearbox health state recognition. First, the challenges caused by the complex vibration characteristics of planetary gearboxes in fault diagnosis are analyzed. Second, according to the popularity of deep learning in planetary gearbox fault diagnosis, we briefly introduce six mainstream algorithms, i.e., autoencoder (AE), deep Boltzmann machine (DBM), convolutional neural network (CNN), transformer, generative adversarial network (GAN), and graph neural network (GNN), and some variants of them. Then, the applications of these methods to planetary gearbox fault diagnosis are reviewed. Finally, the research prospects and challenges in this research are discussed. According to the challenges, a dataset is introduced in this paper to facilitate future investigations. We expect that this paper can provide new graduate students, institutions and companies with a preliminary understanding of methods used in this field. The dataset can be downloaded from https://github.com/Liudd-BJUT/WT-planetary-gearbox-dataset.

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“…114 . The decoder model optimizes low-dimensional node embeddings (accounting for edge sign and direction) to reconstruct the network structure.…”

mentioning

confidence: 99%

DeepDrug: An Expert-led Domain-specific AI-Driven Drug-Repurposing Mechanism for Selecting the Lead Combination of Drugs for Alzheimer’s Disease

Li,

Han,

Kaistha

et al. 2024

Preprint

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Alzheimer’s Disease (AD) significantly aggravates human dignity and quality of life. While newly approved amyloid immunotherapy has been reported, effective AD drugs remain to be identified. Here, we propose a novel AI-driven drug-repurposing method, DeepDrug, to identify a lead combination of approved drugs to treat AD patients. DeepDrug advances drug-repurposing methodology in four aspects. Firstly, it incorporates expert knowledge to extend candidate targets to include long genes, immunological and aging pathways, and somatic mutation markers that are associated with AD. Secondly, it incorporates a signed directed heterogeneous biomedical graph encompassing a rich set of nodes and edges, and node/edge weighting to capture crucial pathways associated with AD. Thirdly, it encodes the weighted biomedical graph through a Graph Neural Network into a new embedding space to capture the granular relationships across different nodes. Fourthly, it systematically selects the high-order drug combinations via diminishing return-based thresholds. A five-drug lead combination, consisting of Tofacitinib, Niraparib, Baricitinib, Empagliflozin, and Doxercalciferol, has been selected from the top drug candidates based on DeepDrug scores to achieve the maximum synergistic effect. These five drugs target neuroinflammation, mitochondrial dysfunction, and glucose metabolism, which are all related to AD pathology. DeepDrug offers a novel AI-and-big-data, expert-guided mechanism for new drug combination discovery and drug-repurposing across AD and other neuro-degenerative diseases, with immediate clinical applications.

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Directed Graph Auto-Encoders

Cited by 10 publications

References 16 publications

PhoGAD: Graph-based Anomaly Behavior Detection with Persistent Homology Optimization

PhoGAD: Graph-based Anomaly Behavior Detection with Persistent Homology Optimization

A review on deep learning in planetary gearbox health state recognition: methods, applications, and dataset publication

DeepDrug: An Expert-led Domain-specific AI-Driven Drug-Repurposing Mechanism for Selecting the Lead Combination of Drugs for Alzheimer’s Disease

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