  Journal Impact Factor is a popular metric for determining the quality of a
journal in academia. The number of citations received by a journal is a crucial
factor in determining the impact factor, which may be misused in multiple ways.
Therefore, it is crucial to detect citation anomalies for further identifying
manipulation and inflation of impact factor. Citation network models the
citation relationship between journals in terms of a directed graph. Detecting
anomalies in the citation network is a challenging task which has several
applications in spotting citation cartels and citation stack and understanding
the intentions behind the citations. In this paper, we present a novel approach
to detect the anomalies in a journal-level scientific citation network, and
compare the results with the existing graph anomaly detection algorithms. Due
to the lack of proper ground-truth, we introduce a journal-level citation
anomaly dataset which consists of synthetically injected citation anomalies and
use it to evaluate our methodology. Our method is able to predict the anomalous
citation pairs with a precision of 100\% and an F1-score of 86%. We further
categorize the detected anomalies into various types and reason out possible
causes. We also analyze our model on the Microsoft Academic Search dataset - a
real-world citation dataset and interpret our results using a case study,
wherein our results resemble the citations and SCImago Journal Rank (SJR)
rating-change charts, thus indicating the usefulness of our method. We further
design `Journal Citation Analysis Tool', an interactive web portal which, given
the citation network as an input, shows the journal-level anomalous citation
patterns and helps users analyze citation patterns of a given journal over the
years.
