Detecting a community in a network is a matter of discerning the distinct features and
connections of a group of members that are different from those in other communities. The …

Graph machine learning (GML) is receiving growing interest within the pharmaceutical and
biotechnology industries for its ability to model biomolecular structures, the functional …

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 Knowledge Tracing (KT) aims to trace the student's state of evolutionary mastery for
a particular knowledge or concept based on the student's historical learning interactions with …

Network embedding assigns nodes in a network to low-dimensional representations and
effectively preserves the network structure. Recently, a significant amount of progresses …

Network embedding (or graph embedding) has been widely used in many real-world
applications. However, existing methods mainly focus on networks with single-typed …

This paper presents a vision for a Disaster City Digital Twin paradigm that can:(i) enable
interdisciplinary convergence in the field of crisis informatics and information and …

Embedding large graphs in low dimensional spaces has recently attracted significant
interest due to its wide applications such as graph visualization, link prediction and node …

Embedding network data into a low-dimensional vector space has shown promising
performance for many real-world applications, such as node classification and entity …

An increasing number of machine learning tasks require dealing with large graph datasets,
which capture rich and complex relationship among potentially billions of elements. Graph …