Network representation learning (NRL) advances the conventional graph mining of social
networks, knowledge graphs, and complex biomedical and physics information networks …

In many domains where data are represented as graphs, learning a similarity metric among
graphs is considered a key problem, which can further facilitate various learning tasks, such …

We present network embedding algorithms that capture information about a node from the
local distribution over node attributes around it, as observed over random walks following an …

Abstract Graph Neural Networks (GNNs) have proven to be useful for many different
practical applications. However, many existing GNN models have implicitly assumed …

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 …

Deep models can be made scale-invariant when trained with multi-scale information.
Images can be easily made multi-scale, given their grid-like structures. Extending this to …

Ethereum is the largest public blockchain by usage. It applies an account-based model,
which is inferior to Bitcoin's unspent transaction output model from a privacy perspective …

Language models (LMs) have demonstrated superior performance in detecting fraudulent
activities on Blockchains. Nonetheless, the sheer volume of Blockchain data results in …

Random walks are at the heart of many existing node embedding and network
representation learning methods. However, such methods have many limitations that arise …

Corporate bankruptcy prediction is a long-standing topic of interest for a variety of
stakeholders. Various prediction methodologies have been proposed to achieve more …