Kiarash Shamsi — accepted AI-conference papers

2025

MiNT: Multi-Network Transfer Benchmark for Temporal Graph Learning

Kiarash Shamsi, Tran Gia Bao Ngo, Razieh Shirzadkhani, Shenyang Huang, Farimah Poursafaei, Poupak Azad +4

NeurIPS 2025poster

Temporal Graph Learning (TGL) aims to discover patterns in evolving networks or temporal graphs and leverage these patterns to predict future interactions. However, most existing research focuses on learning from a single network in isolation, leaving the challenges of within-domain and cross-domain…

Cited by 0 Source ↗code ↗Scholar ↗

2024

GraphPulse: Topological representations for temporal graph property prediction

Kiarash Shamsi, Farimah Poursafaei, Shenyang Huang, Bao Tran Gia Ngo, Baris Coskunuzer, Cuneyt Gurcan Akcora

ICLR 2024poster

Many real-world networks evolve over time, and predicting the evolution of such networks remains a challenging task. Graph Neural Networks (GNNs) have shown empirical success for learning on static graphs, but they lack the ability to effectively learn from nodes and edges with different timestamps.…

Cited by 3 Source ↗PDF ↗code ↗Scholar ↗

2022

Chartalist: Labeled Graph Datasets for UTXO and Account-based Blockchains

Kiarash Shamsi, Friedhelm Victor, Murat Kantarcioglu, Yulia Gel, Cuneyt Gurcan Akcora

NeurIPS 2022accept

Machine learning on blockchain graphs is an emerging field with many applications such as ransomware payment tracking, price manipulation analysis, and money laundering detection. However, analyzing blockchain data requires domain expertise and computational resources, which pose a significant barri…

Cited by 28 Source ↗PDF ↗code ↗Scholar ↗