Graph neural networks (GNNs) are an emerging research field. This specialized deep
neural network architecture is capable of processing graph structured data and bridges the …

Blockchain consensus protocols have been a focus of attention since the advent of Bitcoin.
Although classic distributed consensus algorithms made significant contributions to the …

The field of graph neural networks (GNNs) has seen rapid and incredible strides over the
recent years. Graph neural networks, also known as deep learning on graphs, graph …

Anomalies are rare observations (eg, data records or events) that deviate significantly from
the others in the sample. Over the past few decades, research on anomaly mining has …

Deep learning on graphs has attracted significant interests recently. However, most of the
works have focused on (semi-) supervised learning, resulting in shortcomings including …

Blockchain-based decentralized cryptocurrencies have drawn much attention and been
widely-deployed in recent years. Bitcoin, the first application of blockchain, achieves great …

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 …

Graph convolutional network (GCN) has been successfully applied to many graph-based
applications; however, training a large-scale GCN remains challenging. Current SGD-based …

Abstract Graph Neural Networks (GNNs) have shown great potential in the field of graph
representation learning. Standard GNNs define a local message-passing mechanism which …

Graph neural networks (GNNs) have emerged as a powerful approach for solving many
network mining tasks. However, learning on large graphs remains a challenge--many …