Introduction : We are there members in our group. we study on this IEEE paper. we prepare our own flower on @Giuseppe Santucci. you can look into the presentation. Youtube link: https://youtu.be/riLBp6dZ3Xk What is paper about : We present the Influence Flower, a new visual metaphor for the influence profile of academic entities, including people, projects, institutions, conferences, and journals. While many tools quantify influence, we aim to expose the flow of influence between entities. The Influence Flower is an ego-centric graph, with a query entity placed in the centre. The petals are styled to reflect the strength of influence to and from other entities of the same or different type. For example, one can break down the incoming and outgoing influ- ences of a research lab by research topics. The Influence Flower uses a recent snapshot of Microsoft Academic Graph, consisting of 212 million authors, their 176 million publications, and 1.2 billion citations. An interactive web app, Influence Map, is constructed around this central metaphor for searching and curating visualisa- tions. We also propose a visual comparison method that highlights change in influence patterns over time. We demonstrate through several case studies that the Influence Flower supports data-driven inquiries about the following: researchers’ careers over time; pa- per(s) and projects, including those with delayed recognition; the interdisciplinary profile of a research institution; and the shifting topical trends in conferences. We also use this tool on influence data beyond academic citations, by contrasting the academic and Twitter activities of a researcher.

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Influence flowers of Academic Entities
Luciana Silo
Abhishek Lokhande
Stella Fazioli
Presented by :Academic year
2019 - 2020
Submitted to
Giuseppe Santucci
Minjeong Shin, Alexander Soen, Benjamin T. Readshaw, Stephen M. Blackburn, Mitchell Whitelaw,
Lexing Xie
Australian National University

2. Luciana StellaAbhishek
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ABSTRACT
INTRODUCTION
DATASET
INFLUENCE
FLOWER
CASE STUDY
CONCLUSION

3. What is INFLUENCE FLOWER?
The Influence Flower is an Ego-center graph with a
query entity placed in the center.
The flower petals are formed by curved edges,
whose thickness represent the strengths of
influence in either direction.
The size of alter nodes reflect total volume of
influence.
The color of alter nodes reflect the ratio of
incoming and outgoing influences.
Full circle flower design Flower petals with arrows in the
both ends.
wide band of flower petals. Coloured flower petals

4. What is Influence Flowers of Academic Entities?
A new visual metaphor for the bi-directional influence profile of academic entities,
including: people, projects, institutions, conferences, and journals.
One can search for the influence between wide range of academic entities to form a
project or a group. And also on non-academic data, by comparing and academic
influence of a well-known researchers.
By efficient indexing and caching in the back-end, users can sort and filter with the
influence flower. We can compare snapshots of influence overtime.
WHY? : Paper across disciplines, profiling, changes in conference.

5. How to create Influence Flower for Academic Entities?
Influence flower uses Microsoft Academic Graph
which consist of
We can create 50 Petals, with publication and
citation year range from 1989 - 2018.
An web app, Influence Map is constructed around
this central metaphor for searching and curating
visualizations.
Click here to create Influence flower.

6. THE PAPER EXPLAIN ABOUT!
The flower visualises 25 authors who had
the most citation influence to and from
VAST.
Blue edge references to author’s work by
VAST. Red edge citation made by author to
VAST paper.
The 24 authors with names grey have published in VAST.
The only exception is Alfred Inselberg, who is known and being cited for parallel
coordinates.
Venue-to-author Influence flower of VAST(IEEE conference on Visual Analytics
Science and Technology.)

7. Related work.
This work builds on a rich literature, in particular on:
Academic search engines and libraries: modelled after web search engines or library
catalogues.
Visualization of bibliographic data: has long fascinated visualization researchers as
they are rich, multi-relational, and in a domain that researchers can readily relate to .
The science of science: is an active research area that uses large data sets to study
the mechanism underlying the production of new knowledge.

8. Dataset
We use a dataset to compute influence statistics called MAG (Microsoft Academic Graph)
that covers all research fields and is comprised of six types of academic entities.
Entity types are discovered from structured and unstructured sources.
It provides entity resolution such as merging records from different sources,
deduplication, and disambiguation;
Influence map is based on a MAG data snapshot taken on 2018-06-29. It changes
periodically.
Microsoft Academy uses a large vocabulary of research topics, that are mapped to
papers using machine learning algorithm on paper information, such as title, abstract
and publishing venue.

9. A discussion on data coverage and quality
MAG found better coverage when compared to other popular bibliographical sources such
as SCOPUS and WEB OF SCIENCE.
In MAG for each publication there are two version of citation count.
Algorithmically verified citations.
Estimated citation count.
Two phenomena in the computer science research that may affect citation statistics:
articles appearing within a given venue may be subject to different editorial processes
(especially conferences);
papers are sometimes published as a short conference abstract and then later as a full
paper.
STRATEGY: query MAG with curated paper titles. In fact, 97% of papers from computer
science venues have corresponding entries in MAG!!

10. How Influence Flower works?
Other
paper
Center
Entity
Other
Paper
Incoming influence Outgoing influence
Citing Cited by
To generate nodes and edges of an influence flower, we aggregate the pairwise influence
of entities associated with the papers of the ego.
In dataset, we denote :
E = set of entities
P = set of papers.
The association matrix : A ∈ {0,1}|E|⇥|P| (∴ relation between entities and papers).
A(a) = author
A(v) = venue
A(i) = institution
A(t) = topic

11. How to calculate Influence Score?
Element A(a)
ij =1 (if paper ‘j’ is authored by person ‘i’),Otherwise 0.
Citation matrix = C ∈ {0, 1}|P|⇥|P| (∴ Citation relation between paper).
Cij = 1 (if pi influences pj ( pj cites pi )), otherwise 0.
The total influence can be expressed as multiplication of these indicator matrices. To
obtain influence scores between all entities of type ‘k’ and type ‘l’, where k, l ∈ {a, v, i, t},
we only need to compute : S(k,l) = A(k)CA(l)T
Hence, the influence score between author ‘i’ and topic ‘j’ is calculated. Sij
(a,t)ai
(a) C aj
(t) T
Comparing Influence Flowers : To compare multiple Influence Flowers generated at
different time periods, we created the concept of an anchor flower. We stack two flowers
to allow direct size comparison, unlike other studies.

12. DETAIL VIEW OF INFLUENCE MAP SYSTEM?
It is build around influence flower as the central visual component and it contains three
additional interactive components.
Data
Preprocessing Detail Page
• Display underlying
information
Flower Page
• Calculate
influence score
• Update influence
flower
• Compare flowers
Entity Search
• Search academic
entities
• Aggregate entities
database
Searching and curating a flower : Elasticsearch.
All the scoring and data manipulation : Python 3 with panda libraries.
Visualization of charts and flowers : d3.js.

13. Details behind an edge?
For example, the details page
when clicking the VAST (IEEE
conference on Visual Analytics
Science and Technology) in the
author-to-venue flower of
GIUSEPPE SANTUCCI.
The blue and red columns present VAST papers that
have influenced and have been influenced by
Santucci. Arrows indicate the direction of influence.
The entries are chronologically ordered by the ego’s
own papers (grey column, middle).

14. CASE STUDY
Having described the design of the Influence Flower metaphor and the system for
searching and interacting with the flower, we present five case studies for using Influence
Map:
The career of a researcher.
An example of delayed impact.
The intellectual heritage of a lab.
The evolution of a research community.
Using Influence Flowers beyond the academic context.

15. CASE STUDY
Influence Flower and a career trajectory
Author-to-Author Influence Flower.
A paper with delayed recognition
A paper with delayed recognition, often called a sleeping beauty or a Mendel syndrome.
A publication which received very little or no attention for a while, then was discovered later
and received many citations.
We compare the people and publication venues in her
Influence Flower with those present in her publication
profile and mentioned in her interview transcripts, finding a
few notable correspondences.

16. CASE STUDY
Paper-to-topic Influence Flower
When the term sleeping beauty was first introduced by this
paper was used as an extreme example.
The paper does not draw any attention until it gets its first
citation.
We examine the Santa Fe Institute, an independent nonprofit
research and education centre, in its first 20 years.
In this case study, we use Influence Map to profile the heritage
and contributions a research centre has towards multiple fields.
Interdisciplinary work of a research institute
Institution-to-topic Influence Flower

17. CASE STUDY
Trend change of a venue
Computer science as a field has been having increasingly many publications every year.
In our analysis, we use a series of Influence Flowers to examine the change in topics over
time in WWW.
In this case study we have demonstrated how one can use Influence Map to examine the
changes in research topics of a publication venue.
The Influence Flower metaphor can be applied to data outside of academic graphs.
The interacting Twitter users form the alter nodes. The node sizes are proportional to their
respective number of Twitter followers.
Influence Flowers of social media data

18. CONCLUSION
We develop the Influence Map system to interactively search entities and create
influence flowers, supported by efficient indexing of the entire Microsoft Academic
Graph.
Further, we present case studies illustrating the use of the Influence Flower.
Future work
Visualization function and richness of the flower metaphor.
More versatile comparisons of flowers beyond subsets in time, and to understand
the production and consumption of science at large.

19. LINK TO SLIDE
SHARE
LINK TO CREATE
INFLUENCE FLOWER
TO INFLUENCE MAP
LINK TO DATASET
REFERENCES
THANK YOU