Scientific literature recommender systems (SLRSs) provide papers to researchers according
to their scientific interests. Systems rely on inter-researcher similarity measures that are …

In this paper we evaluate citation networks of authors, publications and journals, constructed
from the ISI Web of Science database (Computer Science categories). Our aim was to find a …

It is important to help researchers find valuable papers from a large literature collection. To
this end, many graph‐based ranking algorithms have been proposed. However, most of …

With the increase in collaboration among researchers of various disciplines, changing the
research topic or working on multiple topics is not an unusual behavior. Several …

The current study has two objectives. First, we explore the characteristics of biological
entities, such as drugs, and their side effects using an author–entity pair bipartite network …

Citation analysis is a popular area of research, which has been usually used to rank the
authors and the publication venues of research papers. With huge number of publications …

We propose a novel probabilistic topic model that jointly models authors, documents, cited
authors, and venues simultaneously in one integrated framework, as compared to previous …

A new challenge, accessing multiple relevant entities, arises from the availability of linked
heterogeneous data. In this article, we address more specifically the problem of accessing …

In this paper, we present a novel approach that models the mutual reinforcing relationship
among papers, authors and publication venues with due cognizance of publication time. We …

Each year many ACM SIG communities will recognize an outstanding researcher through an
award in honor of his or her profound impact and numerous research contributions. This …