Network embedding approaches are gaining momentum to analyse a large variety of
networks. Indeed, these approaches have demonstrated their effectiveness in tasks such as …

While most network embedding techniques model the proximity between nodes in a
network, recently there has been significant interest in structural embeddings that are based …

Attributed graph clustering aims to partition the nodes in a graph into groups such that the
nodes in the same group are close in terms of graph proximity and also have similar attribute …

We systematically evaluate the (in-) stability of state-of-the-art node embedding algorithms
due to randomness, ie, the random variation of their outcomes given identical algorithms …

This paper studies four Graph Neural Network architectures (GNNs) for a graph
classification task on a synthetic dataset created using classic generative models of Network …

While most network embedding techniques model the proximity between nodes in a
network, recently there has been significant interest in structural embeddings that are based …

Recently, link prediction algorithms based on neural embeddings have gained tremendous
popularity in the Semantic Web community, and are extensively used for knowledge graph …

Node embedding methods find latent lower-dimensional representations which are used as
features in machine learning models. In the last few years, these methods have become …

The interactions in Communities Question Answer (CQA) have high dimensionality,
generating dispersed and vast information about the users' behavior. Understanding this …

Palash Goyal is a Senior Research Scientist at Samsung Research America. In 2019, he got
his doctoral degree in Computer Science from University of Southern California under the …