In light of the escalating ubiquity of the Internet, the proliferation of cyber-attacks, coupled
with their intricate and surreptitious nature, has significantly imperiled network security …

Graph similarity computation is crucial in fields such as bioinformatics, eg, identifying
compounds with similar biological activities by comparing molecular structural similarities …

The prediction of ecological toxicity plays an increasingly important role in modern society.
However, the existing models often suffer from poor performance and limited predictive …

In this paper, we introduce a method called graph fusion embedding, designed for multi-
graph embedding with shared vertex sets. Under the framework of supervised learning, our …

Machine Learning (ML)-based optimization approaches emerge as a promising technique
for solving large-scale Mixed Integer Linear Programs (MILPs). However, existing ML-based …

Graph encoder embedding, a recent technique for graph data, offers speed and scalability in
producing vertex-level representations from binary graphs. In this paper, we extend the …

DeepWalk is used to convert nodes in an original graph into equivalent vectors in a latent
space for performing various predictive tasks. To ensure second-order structural similarity …

Background The incidence of graft failure following liver transplantation (LTx) is consistent.
While traditional risk scores for LTx have limited accuracy, the potential of machine learning …

Analyzing large-scale time-series network data, such as social media and email
communications, poses a significant challenge in understanding social dynamics, detecting …

Unexpected toxicity poses a significant impediment to successful entry of drug candidates
into the market. For drug toxicity evaluation, deep learning techniques have exhibited robust …