A survey of imbalanced learning on graphs: Problems, techniques, and future directions
Graphs represent interconnected structures prevalent in a myriad of real-world scenarios.
Effective graph analytics, such as graph learning methods, enables users to gain profound …
Effective graph analytics, such as graph learning methods, enables users to gain profound …
Bert4eth: A pre-trained transformer for ethereum fraud detection
As various forms of fraud proliferate on Ethereum, it is imperative to safeguard against these
malicious activities to protect susceptible users from being victimized. While current studies …
malicious activities to protect susceptible users from being victimized. While current studies …
Sequence-based target coin prediction for cryptocurrency pump-and-dump
With the proliferation of pump-and-dump schemes (P&Ds) in the cryptocurrency market, it
becomes imperative to detect such fraudulent activities in advance to alert potentially …
becomes imperative to detect such fraudulent activities in advance to alert potentially …
Lumos: Heterogeneity-aware federated graph learning over decentralized devices
Graph neural networks (GNN) have been widely deployed in real-world networked
applications and systems due to their capability to handle graph-structured data. However …
applications and systems due to their capability to handle graph-structured data. However …
Gift: Graph-guided feature transfer for cold-start video click-through rate prediction
Short video has witnessed rapid growth in the past few years in e-commerce platforms like
Taobao. To ensure the freshness of the content, platforms need to release a large number of …
Taobao. To ensure the freshness of the content, platforms need to release a large number of …