Influence flower visualizes citation influences among academic entities, including papers, authors, institutions, and research topics.

  • Blue arcs denote incoming influence, with their thickness proportional to the number of references made.
  • Red arcs denote outgoing influence, with their thickness proportional to the number of citations received.
  • Node sizes and colors reflect the volume and composition of incoming and outgoing influence, see "Computing influence scores".
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Computing influence scores

Influence scores is a function of paper citations.
Each edge signifies the flow of influence to and from the center node (which could be a researcher, a publication venue, an insitution, or a research field), the strength of this relation is reflected in the thickness of the edge.
  • The red edges denote the influence the center has towards the outer entities (which can be the same types), i.e., an outer entity citing a paper by the center. The blue edges denote the influence the outer entities have towards the center, i.e., the center cites a paper by an outer entity.
  • The color of the outer nodes denotes the difference between incoming and outgoing influence scores. A blue node indicates that the associated entity has influenced the center more than the center has influenced itself. Likewise, a red node indicates the center has influenced the node's entity more than it has influenced the center.
  • The size of the nodes reflect the total amount of incoming and outgoing influences it has with the center node.

We normalize the influence contribution by the number of entities in the cited paper, to prevent papers associated with a large number of entities (e.g. authors) from creating an overwhelming amount of influence.

Details about influence scores and normalisation choices can be found in Section 4.2 and Appendix B of the Influence Flower paper.

Data source and profile

The current influence statistics are computed from Microsoft Academic Graph (MAG) containing scientific publication records, citation relationships, as well as authors, institutions, journals, conferences, and fields of study. The current influencemap is based on the last available MAG graph snapshot from 2021-12-06. In a nutshell, the whole dataset contains:
  • 271 million scientific publications from year 1800 to 2021.
  • 281 million author IDs and 27 thousand institutions identified by MAG.
  • 292 top-level academic research fields labeled by MAG, such as "labour economics" and "algorithms".
  • Latest paper indexed: December 2021 -- which means some arxiv papers in 2112.xxxxx is indexed, some are not. One of the latest papers with an influence flower is 2112.03000, any paper after that will almost surely return a "no matching paper found" due to lack of data.
  • After the retirement of MAG, we are working on integrating the new data sources from SemanticSchorlar and OpenAlex. The new update will be released soon with later publications.
    The influencemap project is supported by ANU College of Engineering and Computer Science, and ACM SIGMM. We thank Microsoft, Semantic Scholar and OpenAlex for sharing the Academic Graph data, and NECTAR for providing computing infrastructure.

    Our team

    Lead developer:
    Faculty advisors:

    ANU Computational Media Lab

    Building 145, Science Rd
    College of Engineering and Computer Science
    The Australian National University
    Canberra, ACT 2601, Australia

    About the project

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