arXiv subjects

Kishore Vasan

Publications and source records attributed to Kishore Vasan.

5 recordsLinked to original sources

arXiv2024

Human Mobility in the Metaverse

The metaverse promises a shift in the way humans interact with each other, and with their digital and physical environments. The lack of geographical boundaries and travel costs in the metaverse prompts us to ask if the fundamental laws that govern human mobility in the physical world apply. We collected data on avatar movements, along with their network mobility extracted from NFT purchases. We find that despite the absence of commuting costs, an individuals inclination to explore new locations diminishes over time, limiting movement to a small fraction of the metaverse. We also find a lack of correlation between land prices and visitation, a deviation from the patterns characterizing the physical world. Finally, we identify the scaling laws that characterize meta mobility and show that we need to add preferential selection to the existing models to explain quantitative patterns of metaverse mobility. Our ability to predict the characteristics of the emerging meta mobility network implies that the laws governing human mobility are rooted in fundamental patterns of human dynamics, rather than the nature of space and cost of movement.

cs.SI↗

arXiv2023

The Clinical Trials Puzzle: How Network Effects Limit Drug Discovery

The depth of knowledge offered by post-genomic medicine has carried the promise of new drugs, and cures for multiple diseases. To explore the degree to which this capability has materialized, we extract meta-data from 356,403 clinical trials spanning four decades, aiming to offer mechanistic insights into the innovation practices in drug discovery. We find that convention dominates over innovation, as over 96% of the recorded trials focus on previously tested drug targets, and the tested drugs target only 12% of the human interactome. If current patterns persist, it would take 170 years to target all druggable proteins. We uncover two network-based fundamental mechanisms that currently limit target discovery: preferential attachment, leading to the repeated exploration of previously targeted proteins; and local network effects, limiting exploration to proteins interacting with highly explored proteins. We build on these insights to develop a quantitative network-based model of drug discovery. We demonstrate that the model is able to accurately recreate the exploration patterns observed in clinical trials. Most importantly, we show that a network-based search strategy can widen the scope of drug discovery by guiding exploration to novel proteins that are part of under explored regions in the human interactome.

q-bio.QM↗

arXiv2020

SciSight: Combining faceted navigation and research group detection for COVID-19 exploratory scientific search

The COVID-19 pandemic has sparked unprecedented mobilization of scientists, generating a deluge of papers that makes it hard for researchers to keep track and explore new directions. Search engines are designed for targeted queries, not for discovery of connections across a corpus. In this paper, we present SciSight, a system for exploratory search of COVID-19 research integrating two key capabilities: first, exploring associations between biomedical facets automatically extracted from papers (e.g., genes, drugs, diseases, patient outcomes); second, combining textual and network information to search and visualize groups of researchers and their ties. SciSight has so far served over $15K$ users with over $42K$ page views and $13\%$ returns.

cs.IR↗

arXiv2018

Measuring scientific buzz

Keywords are important for information retrieval. They are used to classify and sort papers. However, these terms can also be used to study trends within and across fields. We want to explore the lifecycle of new keywords. How often do new terms come into existence and how long till they fade out? In this paper, we present our preliminary analysis where we measure the burstiness of keywords within the field of AI. We examine 150k keywords in approximately 100k journal and conference papers. We find that nearly 80\% of the keywords die off before year one for both journals and conferences but that terms last longer in journals versus conferences. We also observe time periods of thematic bursts in AI -- one where the terms are more neuroscience inspired and one more oriented to computational optimization. This work shows promise of using author keywords to better understand dynamics of buzz within science.

cs.SI↗

arXiv2018

Is together better? Examining scientific collaborations across multiple authors, institutions, and departments

Collaborations are an integral part of scientific research and publishing. In the past, access to large-scale corpora has limited the ways in which questions about collaborations could be investigated. However, with improvements in data/metadata quality and access, it is possible to explore the idea of research collaboration in ways beyond the traditional definition of multiple authorship. In this paper, we examine scientific works through three different lenses of collaboration: across multiple authors, multiple institutions, and multiple departments. We believe this to be a first look at multiple departmental collaborations as we employ extensive data curation to disambiguate authors' departmental affiliations for nearly 70,000 scientific papers. We then compare citation metrics across the different definitions of collaboration and find that papers defined as being collaborative were more frequently cited than their non-collaborative counterparts, regardless of the definition of collaboration used. We also share preliminary results from examining the relationship between co-citation and co-authorship by analyzing the extent to which similar fields (as determined by co-citation) are collaborating on works (as determined by co-authorship). These preliminary results reveal trends of compartmentalization with respect to intra-institutional collaboration and show promise in being expanded.

cs.SI↗