“…Outlier detection has an extensive range of applications in many fields: bank fraud [2]- [4], video surveillance [5]- [8], network anomalies [9]- [11], finding new celestial objects [12], [13], etc. The available outlier detection algorithms can be broadly classified into: distance-based algorithms [14]- [17], density-based algorithms [18]- [20], clustering-based algorithms [21], [22], statistical methods [23], integration-based methods [24], numerous neural network-based algorithms [25] and graph-based algorithms [26] etc. Most graph algorithms only work on graph-structured data, and the same goes for GCN [27].…”