5 research outputs found
Modeling interdisciplinary interactions among Physics, Mathematics & Computer Science
Interdisciplinarity has over the recent years have gained tremendous
importance and has become one of the key ways of doing cutting edge research.
In this paper we attempt to model the citation flow across three different
fields -- Physics (PHY), Mathematics (MA) and Computer Science (CS). For
instance, is there a specific pattern in which these fields cite one another?
We carry out experiments on a dataset comprising more than 1.2 million articles
taken from these three fields. We quantify the citation interactions among
these three fields through temporal bucket signatures. We present numerical
models based on variants of the recently proposed relay-linking framework to
explain the citation dynamics across the three disciplines. These models make a
modest attempt to unfold the underlying principles of how citation links could
have been formed across the three fields over time.Comment: Accepted at Journal of Physics: Complexit
Duplicate Question Retrieval and Confirmation Time Prediction in Software Communities
Community Question Answering (CQA) in different domains is growing at a large
scale because of the availability of several platforms and huge shareable
information among users. With the rapid growth of such online platforms, a
massive amount of archived data makes it difficult for moderators to retrieve
possible duplicates for a new question and identify and confirm existing
question pairs as duplicates at the right time. This problem is even more
critical in CQAs corresponding to large software systems like askubuntu where
moderators need to be experts to comprehend something as a duplicate. Note that
the prime challenge in such CQA platforms is that the moderators are themselves
experts and are therefore usually extremely busy with their time being
extraordinarily expensive. To facilitate the task of the moderators, in this
work, we have tackled two significant issues for the askubuntu CQA platform:
(1) retrieval of duplicate questions given a new question and (2) duplicate
question confirmation time prediction. In the first task, we focus on
retrieving duplicate questions from a question pool for a particular newly
posted question. In the second task, we solve a regression problem to rank a
pair of questions that could potentially take a long time to get confirmed as
duplicates. For duplicate question retrieval, we propose a Siamese neural
network based approach by exploiting both text and network-based features,
which outperforms several state-of-the-art baseline techniques. Our method
outperforms DupPredictor and DUPE by 5% and 7% respectively. For duplicate
confirmation time prediction, we have used both the standard machine learning
models and neural network along with the text and graph-based features. We
obtain Spearman's rank correlation of 0.20 and 0.213 (statistically
significant) for text and graph based features respectively.Comment: Full paper accepted at ASONAM 2023: The 2023 IEEE/ACM International
Conference on Advances in Social Networks Analysis and Minin
The rise and rise of interdisciplinary research: Understanding the interaction dynamics of three major fields -- Physics, Mathematics & Computer Science
The distinction between sciences is becoming increasingly more artificial -- an approach from one area can be easily applied to the other. More exciting research nowadays is happening perhaps at the interfaces of disciplines like Physics, Mathematics and Computer Science. How do these interfaces emerge and interact? For instance, is there a specific pattern in which these fields cite each other? In this article, we investigate a collection of more than 1.2 million papers from three different scientific disciplines -- Physics, Mathematics, and Computer Science. We show how over a timescale the citation patterns from the core science fields (Physics, Mathematics) to the applied and fast-growing field of Computer Science have drastically increased. Further, we observe how certain subfields in these disciplines are shrinking while others are becoming tremendously popular. For instance, an intriguing observation is that citations from Mathematics to the subfield of machine learning in Computer Science in recent times are exponentially increasing.by Rima Hazra, Mayank Singh, Pawan Goyal, Bibhas Adhikari and Animesh Mukherje