Information Processing Letters

Top Research Topics at Information Processing Letters?

Information Processing Letters mainly deals with areas of study such as Combinatorics, Discrete mathematics, Algorithm, Time complexity and Theoretical computer science. Information Processing Letters explores issues in Combinatorics which can be linked to other research areas like Computational complexity theory, Upper and lower bounds and Set (abstract data type). While Discrete mathematics is the focus of the journal, it also provided insights into the studies of Graph theory, Bounded function and Approximation algorithm.

The work on Algorithm tackled in the journal brings together disciplines like Mathematical optimization and Data structure. It links adjacent topics like Chordal graph with Indifference graph.

• Combinatorics (43.79%)
• Discrete mathematics (33.25%)
• Algorithm (23.79%)

What are the most cited papers published in the journal?

• The particle swarm optimization algorithm: convergence analysis and parameter selection (2137 citations)
• An algorithm for drawing general undirected graphs (2129 citations)
• An efficient algorith for determining the convex hull of a finite planar set (1328 citations)

Research areas of the most cited articles at Information Processing Letters:

The most cited papers facilitate discussions on Combinatorics, Discrete mathematics, Algorithm, Time complexity and Computational complexity theory. The journal publications dive deep in exploring the relationship between the study of Combinatorics and Set (abstract data type). While work presented in the most cited articles provide substantial information on Algorithm, it also covers topics in Simple (abstract algebra), Theoretical computer science and Mathematical optimization.

What topics the last edition of the journal is best known for?

• Artificial intelligence
• Programming language
• Algorithm

The previous edition focused in particular on these issues:

Information Processing Letters is organized to address concerns in the fields of Combinatorics, Graph, Set (abstract data type), Graph (abstract data type) and Discrete mathematics. In addition to Combinatorics research, it aims to explore topics under Planar, Simple (abstract algebra) and Bounded function. The journal explores topics in Graph which can be helpful for research in disciplines like Class (set theory), Cardinality, Lemma (mathematics) and Spanning tree.

The Set (abstract data type) works featured in the journal incorporate elements from Mathematical analysis, Radius, Integer (computer science), Clique (graph theory) and Connected component. The studies on Graph (abstract data type) discussed can also contribute to research in the domains of Spanner, Type (model theory), Pairwise comparison, Projective test and Point (geometry). In it, Heap (data structure), Logical consequence and Key (cryptography) are investigated in conjunction with one another to address concerns in Discrete mathematics research.

The most cited articles from the last journal are:

• List k-colouring P-free graphs: A Mim-width perspective (4 citations)
• Palindromic trees for a sliding window and its applications (2 citations)
• Minimum projective linearizations of trees in linear time (1 citations)

Papers citation over time

A key indicator for each journal is its effectiveness in reaching other researchers with the papers published at that venue.

The chart below presents the interquartile range (first quartile 25%, median 50% and third quartile 75%) of the number of citations of articles over time.

Top authors and change over time

The top authors publishing in Information Processing Letters (based on the number of publications) are:

• Wojciech Rytter (27 papers) absent at the last edition,
• Kurt Mehlhorn (22 papers) absent at the last edition,
• Lih-Hsing Hsu (22 papers) absent at the last edition,
• Gerhard J. Woeginger (22 papers) absent at the last edition,
• Fabrizio Luccio (20 papers) absent at the last edition.

The overall trend for top authors publishing in this journal is outlined below. The chart shows the number of publications at each edition of the journal for top authors.

Top affiliations and change over time

Only papers with recognized affiliations are considered

The top affiliations publishing in Information Processing Letters (based on the number of publications) are:

• University of Warsaw (90 papers) absent at the last edition,
• IBM (88 papers) absent at the last edition,
• University of Waterloo (81 papers) absent at the last edition,
• Tel Aviv University (79 papers) absent at the last edition,
• Technion – Israel Institute of Technology (74 papers) published 1 paper at the last edition, 1 less than at the previous edition.

The overall trend for top affiliations publishing in this journal is outlined below. The chart shows the number of publications at each edition of the journal for top affiliations.

Publication chance based on affiliation

The publication chance index shows the ratio of articles published by the best research institutions in the journal edition to all articles published within that journal. The best research institutions were selected based on the largest number of articles published during all editions of the journal.

The chart below presents the percentage ratio of articles from top institutions (based on their ranking of total papers).Top affiliations were grouped by their rank into the following tiers: top 1-10, top 11-20, top 21-50, and top 51+. Only articles with a recognized affiliation are considered.

During the most recent 2022 edition, 20.59% of publications had an unrecognized affiliation. Out of the publications with recognized affiliations, 7.41% were posted by at least one author from the top 10 institutions publishing in the journal. Another 7.41% included authors affiliated with research institutions from the top 11-20 affiliations. Institutions from the 21-50 range included 7.41% of all publications and 77.78% were from other institutions.

Returning Authors Index

A very common phenomenon observed among researchers publishing scientific articles is the intentional selection of journals they have already attended in the past. In particular, it is worth analyzing the case when the authors participate in the same journal from year to year.

The Returning Authors Index presented below illustrates the ratio of authors who participated in both a given as well as the previous edition of the journal in relation to all participants in a given year.

Returning Institution Index

The graph below shows the Returning Institution Index, illustrating the ratio of institutions that participated in both a given and the previous edition of the conference in relation to all affiliations present in a given year.

The experience to innovation index

Our experience to innovation index was created to show a cross-section of the experience level of authors publishing in a journal. The index includes the authors publishing at the last edition of a journal, grouped by total number of publications throughout their academic career (P) and the total number of citations of these publications ever received (C).

The group intervals were selected empirically to best show the diversity of the authors' experiences, their labels were selected as a convenience, not as judgment. The authors were divided into the following groups:

• Novice - P < 5 or C < 25 (the number of publications less than 5 or the number of citations less than 25),
• Competent - P < 10 or C < 100 (the number of publications less than 10 or the number of citations less than 100),
• Experienced - P < 25 or C < 625 (the number of publications less than 25 or the number of citations less than 625),
• Master - P < 50 or C < 2500 (the number of publications less than 50 or the number of citations less than 2500),
• Star - P ≥ 50 and C ≥ 2500 (both the number of publications greater than 50 and the number of citations greater than 2500).

The chart below illustrates experience levels of first authors in cases of publications with multiple authors.

Career Prospects in Academia

For researchers and academics who have established an impressive portfolio in the fields of combinatorics, discrete mathematics, and algorithm studies, opportunities abound in various sectors. One of these countless opportunities would be a career in teaching. Becoming a high school history teacher, for instance, is a viable career option where one can utilize extensive information derived from these research topics while shaping young minds. Not only will you get the satisfaction of impacting knowledge, but the remuneration is also competitive. For anyone interested in exploring this path, a deep dive into the specifics such as the qualifications needed and the remuneration structure would be beneficial. Take a look at this informational guide on how much does a high school history teacher make in California to get a better understanding of the prospects in academia. A career in teaching is not only rewarding but also provides a platform to share your knowledge and passion with the younger generation. This, coupled with the fact that there is a high demand for experienced educators, makes it an option worth considering for researchers in the aforementioned study fields.