A pinboard by
Mayank Singh

PhD student, IIT Kharagpur


Our work presents evolution models to describe the citation stealing process in research papers

How does a research paper loses its popularity as it gets old? Where does its incoming citations diverted to? Who is benefited most if a similar paper is getting old? We answer some of these interesting questions in our work.


On the categorization of scientific citation profiles in computer sciences

Abstract: A common consensus in the literature is that the citation profile of published articles in general follows a universal pattern - an initial growth in the number of citations within the first two to three years after publication followed by a steady peak of one to two years and then a final decline over the rest of the lifetime of the article. This observation has long been the underlying heuristic in determining major bibliometric factors such as the quality of a publication, the growth of scientific communities, impact factor of publication venues etc. In this paper, we gather and analyze a massive dataset of scientific papers from the computer science domain and notice that the citation count of the articles over the years follows a remarkably diverse set of patterns - a profile with an initial peak (PeakInit), with distinct multiple peaks (PeakMul), with a peak late in time (PeakLate), that is monotonically decreasing (MonDec), that is monotonically increasing (MonIncr) and that can not be categorized into any of the above (Oth). We conduct a thorough experiment to investigate several important characteristics of these categories such as how individual categories attract citations, how the categorization is influenced by the year and the venue of publication of papers, how each category is affected by self-citations, the stability of the categories over time, and how much each of these categories contribute to the core of the network. Further, we show that the traditional preferential attachment models fail to explain these citation profiles. Therefore, we propose a novel dynamic growth model that takes both the preferential attachment and the aging factor into account in order to replicate the real-world behavior of various citation profiles. We believe that this paper opens the scope for a serious re-investigation of the existing bibliometric indices for scientific research.

Pub.: 21 Mar '15, Pinned: 29 Jul '17

Fitness networks for real world systems via modified preferential attachment

Abstract: Complex networks are virtually ubiquitous, and the Barabási and Albert model (BABA model) has became an acknowledged standard for the modelling of these systems. The so-called BABA model is a kind of preferential attachment growth model based on the intuitive premise that popularity is attractive. However, preferential attachment alone is insufficient to describe the diversity of complex networks observed in the real world. In this paper we first use the accuracy of a link prediction method, as a metric for network fitness. The link prediction method predicts the occurrence of links consistent with preferential attachment, the performance of this link prediction scheme is then a natural measure of the ”preferential-attachment-likeness” of a given network. We then propose several modification methods and modified BABA models to construct networks which more accurately describe the fitness properties of real networks. We find that all features assortativity, degree distribution and rich-club formation can play significant roles for the network construction and eventual structure. Moreover, link sparsity and the size of a network are key factors for network reconstruction. In addition, we find that the structure of the network which is limited by geographic location (nodes are embedded in a Euclidean space and connectivity is correlated with distances) differs from other typical networks. In social networks, we observe that the high school contact network has similar structure as the friends network and so we speculate that the contact behaviours can reflect real friendships.

Pub.: 18 Jan '17, Pinned: 29 Jul '17