Sandipan Sikdar

TL;DR: This paper systematically studies the temporal interaction patterns of communities using a large scale citation network (directed and unweighted) of computer science to understand how each individual community grows/shrinks, becomes important over time.

TL;DR: This work proposes for the first time a suite of metrics that can be used to perform post-hoc analysis of the temporal communities of a large-scale citation network of the computer science domain, and quantifies the impact of a field, the influence imparted by one field on the other, the distribution of the “star” papers and authors, the degree of collaboration and seminal publications to characterize such research trends.

TL;DR: It is shown that even if the network structure at a future time point is not available one can still manage to estimate its properties, and how such prediction scheme can be used to launch targeted attacks on temporal networks.

TL;DR: The introduction of ‘POLAR’ — a framework that adds interpretability to pre-trained word embeddings via the adoption of semantic differentials and shows that the interpretable dimensions selected by the framework align with human judgement.

TL;DR: This paper proposed a framework that adds interpretability to pre-trained word embeddings via the adoption of semantic differentials, which is a psychometric construct for measuring the semantics of a word by analysing its position on a scale between two polar opposites (e.g., cold -- hot, soft -- hard).