A Novel Feature Selection-based Algorithm for Medical Correlation of High Dimensional Data

Patil, Shital; Bhosale, Surendra

doi:10.1109/globconet56651.2023.10150163

Cited by 1 publication

(1 citation statement)

References 18 publications

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“…This methodology addresses the need to differentiate subtle but crucial variations between normal and seemingly normal-like abnormal nodes, enhancing the effectiveness of anomaly detection in attributed networks. Additionally, in high-dimensional data, there's a significant risk of overfitting where the model learns patterns specific to the training data, failing to generalize to new data, whereas the approaches using the reconstruction error rely on simple, direct mappings [28], [30], [31], [32].…”

Section: Introductionmentioning

confidence: 99%

Detecting Anomalies in Attributed Networks Through Sparse Canonical Correlation Analysis Combined With Random Masking and Padding

Khan,

Ishrat,

Neyaz Khan

et al. 2024

IEEE Access

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Attributed networks are prevalent in the current information infrastructure, where node attributes enhance knowledge discovery. Anomaly detection in attributed networks is gaining attention for its potential uses in cybersecurity, finance, and healthcare. Recognizing the complicated relationship between node attributes and network topology is crucial for attributed network embedding and anomaly detection. Nevertheless, there are few approaches available to directly represent the relationship between these two perspectives of the node property and the network topology. Approaches utilizing the reconstruction error rely on straightforward, simple mappings, which introduce a substantial risk of overfitting in high-dimensional data, wherein the model acquires patterns that are exclusive to the training data and fails to generalize to new data. To do this, we suggest a new way to find graph anomalies on attributed networks using random masking and padding along with sparse canonical correlation analysis. Motivated by the limitations of existing methodologies in effectively addressing these challenges, our research introduces a novel methodology for anomaly detection in attributed networks by leveraging Sparse Canonical Correlation Analysis (SCCA) in conjunction with Random Masking and Padding (RMP). This dual approach uniquely addresses the challenges of high-dimensional data and the sparsity of attributes, which are prevalent issues in anomaly detection. Unlike previous works that primarily focus on either dimensionality reduction or attribute sparsity independently, our method synergizes these aspects to enhance detection performance. Initially, we randomly mask and pad nodes in the attributed network and use the Graph Convolutional Network (GCN) to map them to latent space. Next, we optimize the distribution alignment of node attributes and graph structure latent space representations by using Kullback-Leibler (KL) divergence regularization, which increases their comparability. Finally, we use sparse canonical correlation analysis (SCCA) to quantify the correlation between node attributes and network structure views in latent space. SCCA incorporates sparsity by making the model choose fewer variables, which adds another level of complexity. It improves interpretability and reduces overfitting in high-dimensional data analysis by highlighting only the key variables. To optimize our model, we maximize the correlation between attribute and structural aspects of normal nodes, and anomalies are detected by measuring the correlation between these two views. Our approach is the first of its kind to provide a novel remedy to the fundamental problems preventing efficient and accurate anomaly identification, thereby establishing a new standard in this field. The proposed model has been extensively tested on four real-world datasets, and its effectiveness has been demonstrated in comparison to state-of-the-art approaches. The empirical evaluation across multiple benchmark datasets validates the potential of the proposed appr...

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