Temporal Graph Learning
Temporal GNN for Blockchain Fraud Detection
A graph-learning research build on modelling fraud as a relational and time-dependent problem.
Temporal graph-learning research build
Conceptual temporal-graph illustration
The diagram explains the model choice without claiming an unpublished benchmark outcome.
Scope
Role and problem
My role: Built the temporal graph-learning pipeline and XGBoost comparison path.
Fraud is relational and time-dependent. Static tabular features can miss how transactions evolve across a network.
Architecture
System flow
Transaction records
Dynamic graph construction
Temporal encoding
Attention-based message passing
Dual-task learning
XGBoost comparison path
Evaluation rerun
Evidence
Measured signals
TGAT-style
Temporal architecture
Temporal encodings and attention-based message passing.
Dual-task
Learning design
Fraud-oriented temporal graph-learning pipeline.
XGBoost
Comparison path
A traditional benchmark route is included for reproducible comparison.
Public scope: The public case study documents architecture and benchmark design without claiming unpublished comparative performance.
Contribution
- Modelled blockchain transactions as a dynamic graph.
- Implemented temporal encodings and attention-based message passing.
- Created a benchmark path against XGBoost without pretending an unpublished comparison is evidence.
Lessons
- Model choice should follow the structure of the problem.
- A benchmark is only valuable when the protocol is reproducible.
- Architecture and benchmark claims should remain clearly separated.
Limitations
- No comparative TGAT-versus-XGBoost performance claim is reported publicly.
- The graph illustration is conceptual rather than dataset-specific.
- The public scope is methodological.
Stack
- PyTorch
- NetworkX
- TGAT
- Temporal GNNs
- XGBoost
- Fraud Detection