Browsing by Author "Singh, Divit P."

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    CrowdLayout: Crowdsourced Design and Evaluation of Biological Network Visualizations
    Singh, Divit P.; Lisle, Lee; Murali, T. M.; Luther, Kurt (ACM, 2018-04)
    Biologists often perform experiments whose results generate large quantities of data, such as interactions between molecules in a cell, that are best represented as networks (graphs). To visualize these networks and communicate them in publications, biologists must manually position the nodes and edges of each network to reflect their real-world physical structure. This process does not scale well, and graph layout algorithms lack the biological underpinnings to offer a viable alternative. In this paper, we present CrowdLayout, a crowdsourcing system that leverages human intelligence and creativity to design layouts of biological network visualizations. CrowdLayout provides design guidelines, abstractions, and editing tools to help novice workers perform like experts. We evaluated CrowdLayout in two experiments with paid crowd workers and real biological network data, finding that crowds could both create and evaluate meaningful, high-quality layouts. We also discuss implications for crowdsourced design and network visualizations in other domains.
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    GraphCrowd: Harnessing the Crowd to Lay Out Graphs with Applications to Cellular Signaling Pathways
    Singh, Divit P. (Virginia Tech, 2016-07-05)
    Automated analysis of networks of interactions between proteins has become pervasive in molecular biology. Each node in such a network represents a protein and each edge an interaction between two proteins. Nearly every publication that uses network analysis includes a visualization of a graph in which the nodes and edges are laid out in two dimensions. Several systems implement multiple types of graph layout algorithms and make them easily accessible to scientists. Despite the existence of these systems, interdisciplinary research teams in computational biology face several challenges in sharing computed networks and interpreting them. This thesis presents two systemsGraphSpace and GraphCrowdthat together enhance network-based collaboration. GraphSpace users can automatically and rapidly share richly- annotated networks, irrespective of the algorithms or software used to generate them. A user may search for networks that contain a specific node or edge, or a collection of nodes and edges. Users can manually modify a layout, save it, and share it with other users. Users can create private groups, invite other users to join groups, and share networks with group members. Upon publication, researchers may make networks public and provide a URL in the paper. GraphCrowd addresses the challenging posed by automated layout algorithms, which incorporate almost no knowledge of the biological information underlying the networks. These algorithms compel researchers to use their knowledge and intuition to modify the node and edge positions manually to bring out salient features. GraphCrowd focuses on signaling networks, which connect proteins that represent a cells response to external signals. Treating network layout as a design problem, GraphCrowd explores the feasibility of leveraging human computation via crowdsourcing to create simplified and meaningful visualizations. GraphCrowd provides a streamlined interface that enables crowd workers to easily manipulate networks to create layouts that follow a specific set of guidelines. GraphCrowd also implements an interface to allow a user (e.g., an expert or a crowd worker) to evaluate how well a layout conforms to the guidelines. We use GraphCrowd to address two research questions: (i) Can we harness the power of crowdsourcing to create simplified, biologically meaningful visualizations of signaling networks?(ii) Can crowd workers rate layouts similarly to how an expert with domain knowledge would rate them? We design two systematic experiments that enable us to answer both questions in the affirmative. This thesis establishes crowdsourcing as a powerful methodology for laying out complex signaling networks. Moreover, by developing appropriate domain-specific guidelines for crowd workers, GraphCrowd can be generalized to a variety of applications.
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    KindaRight
    Lopez-Gomez, Austin; Singh, Divit P. (2014-05-08)
    Concept: KindaRight is a collaborative, open network for artists of all disciplines. We emphasize collaboration between artists of all disciplines because we firmly believe that art comes from inspiration, and inspiration comes from people. The more you know and the more you see will help artists produce better, more beautiful pieces of art. We believe that the best people qualified to critique art are artists themselves, which is why KindaRight also revolves around a merit system which shows your status as an artist weighted for both how many people have liked you, and the respective merit of those people. Finally, we are first and foremost a network: for connecting those creating art to those buying art; to discover new art and new talent; and where the entire art community can work together. Current Status: As of the end of this semester we have implemented a full user experience for uploading and sharing photographs. We plan to continue this project and implement a design that is more closely related to our vision. We have included various milestone markers including our midterm and final presentations that detail our status at those points in time respectively. We also included our poster from VTURCS which gives a good overall description of our project and where our future works will be focused. Lastly we have included our final report which is a comprehensive documentation of everything that we have built this semester. Eventually, our website will be open: kindaright.com