Graph analytics can lead to better quantitative understanding and control of complex
networks, but traditional methods suffer from the high computational cost and excessive …

With the rapid growth of digital publishing, harvesting, managing, and analyzing scholarly
information have become increasingly challenging. The term Big Scholarly Data is coined …

A scholarly recommendation system is an important tool for identifying prior and related
resources such as literature, datasets, grants, and collaborators. A well-designed scholarly …

Scholarly big data, which is a large-scale collection of academic information, technical data,
and collaboration relationships, has attracted increasing attentions, ranging from industries …

Recommender system (RS) has emerged as a major research interest that aims to help
users to find items online by providing suggestions that closely match their interest. This …

One of the greatest challenges faced by industries today is corrosion and of which, one of
the most vital forms is stress corrosion cracking (SCC). It brings highest forms of risks to the …

Recently, there has been a shifting focus of organizations and governments towards
digitization of academic and technical documents, adding a new facet to the concept of …

At present, community detection methods are mostly focused on the investigation at
unweighted networks. However, real-world networks are always complex, and unweighted …

Recommender systems have been widely used in various domains including movies, news,
music with an aim to provide the most relevant proposals to users from a variety of available …

Physics-informed machine learning (PIML) has emerged as a promising new approach for
simulating complex physical and biological systems that are governed by complex …