Graph representation learning aims to effectively encode high-dimensional sparse graph-
structured data into low-dimensional dense vectors, which is a fundamental task that has …

Scholarly data is growing continuously containing information about the articles from a
plethora of venues including conferences, journals, etc. Many initiatives have been taken to …

Name disambiguation---a fundamental problem in online academic systems--is now facing
greater challenges with the increasing growth of research papers. For example, on AMiner …

Graph clustering has been a hot research topic and is widely used in many fields, such as
community detection in social networks. Lots of works combining auto-encoder and graph …

Author-level scientometric indicators are an important tool in individual and institutional-
based research assessment and require high-quality author-publication profiles. To address …

Scholarly knowledge graphs (SKG) are knowledge graphs representing research-related
information, powering discovery and statistics about research impact and trends. Author …

Name ambiguity is a prevalent problem in scholarly publications due to the unprecedented
growth of digital libraries and number of researchers. An author is identified by their name in …

This article studies the problem of semi-supervised learning on graphs, which aims to
incorporate ubiquitous unlabeled knowledge (eg, graph topology, node attributes) with few …

While Graph Neural Network (GNN) has shown superiority in learning node representations
of homogeneous graphs, leveraging GNN on heterogeneous graphs remains a challenging …

[Objective] This paper reviews research on author name disambiguation techniques for the
academic literature, aiming to provide references for future studies.[Coverage] A total of 51 …