The modern science of networks has made significant advancement in the modeling of
complex real-world systems. One of the most important features in these networks is the …

Community detection is the process of grouping strongly connected nodes in a network.
Many community detection methods for un-weighted networks have a theoretical basis in a …

We introduce a method to train Quantized Neural Networks (QNNs)--neural networks with
extremely low precision (eg, 1-bit) weights and activations, at run-time. At traintime the …

E-reading has become more popular by making the number of book readers high in number.
With online book reading websites, it is much simpler to read any book at any time by simply …

With the enormous growth in the volume of online data, users are flooded with a gigantic
amount of information. This has made the task of Recommender systems (RSs) even more …

Recommendation systems (RSs) are used by different e-commerce sites like Amazon, eBay,
etc., for suggesting relevant recommendations based upon users' preferences or items …

Due to the rapid changes in users' preferences over time, it becomes increasingly important
to focus on the temporal evolution of the users' behavioral patterns to capture the most …

Abstract Knowledge skills in the ICT-industry always evolve. With the vast variety of jobs
available, it is unlikely to educate students with skills to fit every job-requirement. This issue …

Deep community can be detected by removing noise nodes or edges from a network. A
centrality measure, named local Fiedler vector centrality is proposed for deep community …

As user preferences rapidly and continually evolve, it becomes crucial to incorporate these
temporal dynamics in the design of recommender systems. This paper proposes a novel …