Visual Analysis of Scientific Discoveries and Knowledge Diffusion

Chaomei Chen1,2

1College of Information Science and Technology, Drexel University2WISELAB, Dalian University of TechnologyEmail: chaomei.chen@drexel.edu

Outline1. Introduction

– Visual analytics– Motivation of the work– Grand challenges

2. The nature of insight– A recurring theme– A mechanism of discovery

3. An explanatory theory– Principles

• Computational properties– Examples

• Scientific discovery• Complex network analysis• Knowledge diffusion and information foraging

4. Conclusions

Visual Analytics

• The aim is to support analytical reasoning activities.– Detect the expected– Discover the unexpected *

• How does it differ from traditional information visualization?– Insight– Actionability– Example

• Air traffic control versus information search

– Visual analytics emphasizes goal-driven problem solving

Analytical Reasoning

• Analyze a network– Decompose a graph into various components– Identify the nature of individual components and

how they are interconnected– Interpret and make sense of what is visualized• Categorization, aggregation

Scientific discoveries and knowledge diffusion

• It is not enough to show what they look like.• One has to explain why they are structured

and behaved as they are.• Example– finding emerging trends and hot topics in scientific

literature

• Large-scale and periodical research assessment and evaluation in the UK, Australia, …

• Young researchers joining in industrial players such as Thomson Reuters

Grand Challenge 1: Maintaining a Long-Term Focus on Quality

1. Focus on quality in a longer term– Understand the nature of transformative

discoveries in science– Understand factors that influence the diffusion of

scientific knowledge– Improve our ability to evaluate the significance of

scientific discoveries

Grand Challenge 2: Babylon chaos

• Understand trends and patterns of scientific change across different fields of studies of science

• Historical• Philosophical• Sociological• Statistical• Mathematical• Literature-based• Citation-based

• Now different theories are different theories

Grand Challenge 3:Social Computing

• The overwhelming rate and volume of data collected and stored• The need for making sense of multi-source, heterogeneous data from multiple

perspectives• The need for communication across disciplines and professions• Enabling techniques and data should be made to accessible to everyone

– Analysts, scholars, scientists, policy makers, tax payers– Routinely and repeatedly analyze and synthesize– Social and cyber-enabled sense making

• Examples:– CiteULike– ManyEyes– People to Patent– Amazon book reviews– SkyWalker

Grand Challenge 4:Tightly Couple Science and Studies of Science

• The majority of scientometric , bibliometric, and informetric studies are inward-looking.– Few have set their goal to influence scientists

directly!• Exceptional Examples:– Literature-based discovery– Cyber-enabled discovery

Motivations

• What do creative ideas have in common?– Sociologists:

• Structural properties in social networks

– Philosophers:• Involving conflicting and competing views

– Biomedical Scientists:• They are quickly recognized and time tested

• Are there similar properties in scientific networks? • Will they help us detect transformative discoveries in

science?• Will they help us explain why and how these discoveries are

special?

SDSS

Chen, C., Zhang, J., Vogeley, M. S. (2009) Mapping the global impact of Sloan Digital Sky Survey. IEEE Intelligent Systems, 24(4), 74-77.

Chen, C., Zhang, J., Vogeley, M. S. (2009) Mapping the global impact of Sloan Digital Sky Survey. IEEE Intelligent Systems, 24(4), 74-77.

Patterns of Growth …

• Aha!

MalloneeS_1996 has a burst rate of 3.8057, centrality of 0.70, and sigma of 0.72. The two citation peaks correspond to the Oklahoma City Bombing and the September 11

Terrorist Attacks.

1997 2001

WassermanS_1994 has a period of citation burst between 1998 and 2000.

1998 2000

Nobel Prize Winner: Gene TargetingA Sticky Effect

Next

• Now we know what structural and temporal properties are potentially good.

• And we’d be better off to think outside the box if we want to be creative.

• The next question:– What can we do to facilitate thinking outside the

box?– In other words, how do we increase the

actionability in various stages of the process?

Boundary Objects• How communications can take place effectively among participants who

have heterogeneous perspectives, trainings, and preferences?– Typically, scientists in interdisciplinary collaboration often find themselves in

such situations. • Boundary objects are

– stable enough to maintain its own identify during the course of communication

– volatile enough to preserve rooms for imagination• A map is a good example of a boundary object ：

– many layers of information– leaves much room for exploration from a wide range of different perspectives

• It is the freedom of instantiating ones’ own interpretations that facilitates communications between participants who may not have a clear understanding of the other side.

4. Conclusions• 4 grand challenges

– Focus on quality– Babylon chaos– Social computing– Tight coupling

• 2 principles– Structural– Temporal

• 1 intermediate– Boundary objects

• In summary, visual analysis requires a different perspective to identify what is important and what to do with it.

Acknowledgements

• Drexel University, my colleagues and my graduate research assistants, especially James Zhang and Don Pellegrino

• NSF for funding the research• Chinese Change Jiang Scholar Program and the

WISELab at Dalian University, China• Thomson Reuters