Logical reasoning is of vital importance to natural language understanding. Previous studies
either employ graph-based models to incorporate prior knowledge about logical relations, or …

The prediction of drug-target interactions aims to identify potential targets for the treatment of
new and rare diseases. The large number of unknown combinations between drugs and …

We consider the diffusive motion of a particle performing a random walk with Lévy
distributed jump lengths and subject to a resetting mechanism, bringing the walker to an …

Finding node correspondence across networks, namely multi-network alignment, is an
essential prerequisite for joint learning on multiple networks. Despite great success in …

Information networks are ubiquitous in many applications and analysis on such networks
has attracted significant attention in the academic communities. One of the most important …

The Heterogeneous Information Network (HIN) is a graph data model in which nodes and
edges are annotated with class and relationship labels. Large and complex datasets, such …

Social networks, sensor networks, biological networks, and many other information networks
can be modeled as a large graph. Graph vertices represent entities, and graph edges …

Background We propose an efficient and biologically sensitive algorithm based on repeated
random walks (RRW) for discovering functional modules, eg, complexes and pathways …

Multi-agent reinforcement learning (MARL) is a promising algorithm for traffic signal control
(TSC), and graph neural networks make a further improvement on its learning capacity …

Training Graph Neural Networks (GNNs) incrementally is a particularly urgent problem,
because real-world graph data usually arrives in a streaming fashion, and inefficiently …