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Temporal Graph Learning · Fraud Detection

Temporal GNN for Blockchain Fraud Detection

A temporal-graph research build comparing TGAT-style structural reasoning with an optimized XGBoost baseline under a documented synthetic-validation protocol.

Synthetic-validation benchmark · modelling trade-offs clearly labelled

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Temporal-graph modelling trade-off

TGAT improves predictive quality in the documented synthetic-validation run while requiring greater latency and memory than XGBoost.

Scope

Role and problem

My role: Built and documented a temporal-graph modelling pipeline and comparative validation path. Public claims are limited to the uploaded synthetic-validation report.

Fraud and anomalous interactions are relational and time-dependent. Flattened tabular features can be fast, but they may lose higher-order structural context as interactions evolve across a network.

Architecture

System flow

01

Temporal interaction stream

02

Rolling tabular features

03

XGBoost baseline

04

Continuous-time encoding

05

TGAT-style attention

06

Dynamic link prediction

07

Latency and memory trade-off analysis

Evidence

Measured signals

98.42%

Wikipedia-profile TGAT AUC-ROC

Synthetic-validation result from the uploaded comparative report.

94.20%

Enterprise-profile TGAT AUC-ROC

Synthetic-validation result on the higher-complexity transaction profile.

31.5–78.9 ms

TGAT latency range

Per-batch inference latency across the three synthetic-validation profiles; XGBoost remains substantially faster.

Public scope: The public benchmark is a synthetic-validation run. It demonstrates modelling trade-offs and pipeline behaviour; it is not presented as production-system evidence.

Published Evidence

Selected artifacts.

Charts, screenshots, and media artifacts supporting this case study.

Synthetic validation benchmark table comparing TGAT and XGBoost

image evidence

TGAT versus XGBoost benchmark table

Synthetic-validation benchmark across Wikipedia interaction, Reddit hyperlink, and enterprise transaction profiles.

Bar chart comparing TGAT and XGBoost AUC ROC values

image evidence

TGAT and XGBoost AUC-ROC comparison

AUC-ROC comparison from the uploaded synthetic-validation report.

Bar chart comparing TGAT and XGBoost inference latency

image evidence

Inference-latency trade-off

Per-batch latency comparison from the uploaded synthetic-validation report. Lower is faster.

Contribution

  • Modelled dynamic interactions as a temporal graph.
  • Built a TGAT-style validation path with continuous-time encodings and attention-based message passing.
  • Compared predictive quality, latency, and memory trade-offs against a tabular XGBoost baseline.
  • Label the public benchmark as synthetic validation rather than overstating production evidence.

Lessons

  • Model choice should follow the structure of the problem.
  • Higher predictive quality can carry meaningful latency and memory costs.
  • Synthetic validation is useful when its boundary is explicit and the protocol is documented.

Limitations

  • The public benchmark is a synthetic-validation run rather than a live production benchmark.
  • The enterprise transaction profile is a representative validation scenario, not a disclosed client dataset.
  • Deployment decisions still require dataset-specific reruns, calibration, and operational testing.

Stack

  • PyTorch
  • NetworkX
  • TGAT
  • Temporal GNNs
  • XGBoost
  • Fraud Detection
  • Synthetic Validation