Graph Convolutional Networks (GCNs) have recently been shown to be quite successful in
modeling graph-structured data. However, the primary focus has been on handling simple …

Nodes in a multiplex network are connected by multiple types of relations. However, most
existing network embedding methods assume that only a single type of relation exists …

This paper focuses on graph-level representation learning that aims to represent graphs as
vectors that can be directly utilized in downstream tasks such as graph classification. We …

Word embeddings have been widely adopted across several NLP applications. Most
existing word embedding methods utilize sequential context of a word to learn its …

Analytical theories suggest that higher-quality data can lead to lower test errors in models
trained on a fixed data budget. Moreover, a model can be trained on a lower compute …

Network embedding has recently garnered attention due to the ubiquity of the networked
data in the real-world. A network is useful for representing the relationships among objects …

A graph can represent a complex organization of data in which dependencies exist between
multiple entities or activities. Such complex structures create challenges for machine …

Predicting properties of nodes in a graph is an important problem with applications in a
variety of domains. Graph-based Semi Supervised Learning (SSL) methods aim to address …

Abstract Knowledge graph completion (KGC) has attracted increasing attention in recent
years, aiming at complementing missing relationships between entities in a Knowledge …

In recent years, data quality has emerged as an important factor for training massive models.
Analytical theories suggest that higher-quality data can lead to lower test errors in models …