1. Multi-level predictive analytics and motif discovery across large dynamic spatiotemporal networks and in complex sociotechnical systems: An organizational genetics approach
Sunil Wattal, Fox School of Business
with
Rob Kulathinal, College of Science and Technology
Zoran Obradovic, College of Science and Technology
Youngjin Yoo, Case Western University

2. Motivation massive amounts of digital trace data
human actors and man-made artifacts comprise complex socio-technical systems
massively interconnected

4. Digital Trace Data massive unstructured granular heterogenous dynamic
performative

5. Need for new methods Limitations of traditional econometric models
Need for newer approaches
parallel with evolutionary systems biology

6. Research Questions
can we characterize a stream of digital trace data from a complex socio-technical system with finite genetic elements?
can we explain and predict the behavior of complex socio-technical systems (i.e., phenotypes) based on the underlying pattern of “behavioral gene” (i.e., genotypes) interactions?
how do mutational input, gene flow, and recombination in “behavioral genes” affect the evolution of socio-technical systems?

7. Construction of Behavioral Genes and Behavioral Genomes
Structured
user-created data
Unstructured
user-created data
Structured
sensor data
Construction of Behavioral Genes and Behavioral Genomes
Sequence Analysis of Behavioral Genes
Construction of Behavioral Gene Co-expression Networks
Multi-level Dynamic Analysis of Gene Co-Expression Networks

8. Data transformation and alignment
FiA generic model of digital trace data with encoding based on behavioral ontology. Colors, letters, and numbers can represent activities, objects, or individuals.
An example alignment using our GitHub data.
Time-stamped digital trace data are transformed into analyzable sequence data, which capture the four elements of each event: actor, action, object, and time. Blocks are colored differently according to each activity type; numbers 1 and 2 indicate whether an activity is executed by a core or peripheral developer.

9. Motif discovery Example motif discovery using GitHub data.
Frequently repeated subsequences can be detected through sequence mining techniques. Those subsequences jointly form patterns of the project sequences while each represents related but difference social meanings.

10. The phylogeny of two projects on GitHub.
Each node in the phylogenetic tree represents one version of the project’s source code. The node is labeled as “developer’s name+creation time”. As we can see, jQuery-Box-Slider (A) has three major groups of revisions and most revisions fall into the group at the bottom.
While for the jQuery-Fast-Click project (B), there are also three major groups, however, each groups have relatively similar numbers of revisions. Such difference could be contributed by the individual developer vs. various developers involved in each project. 

11. Network Analysis
An example network using WordPress data from December Network showing 302 WordPress (internal) and external APIs connected by 7,894 edges. Red represents internal APIs; blue represents external API interactions; green is all other APIs.
Although the pattern of interactions among genes is non-linear and selective, certain combinations of genes are repeatedly used across a diverse set of functions.

12. Heat Map
Plugin topology overlap matrix plots across time. Topology overlab matricies for API clusters. API clusters are represented by the colored blocks on the axes of heatmaps. Warmer colors within the heatmaps indicate higher similarity between APIs.

13. Future Outcomes
Predicting the future performance of complex socio-technical systems with dynamic individual components based on the on-going behaviors of the individual components
Predicting “what’s next” for individual components’ behaviors

14. Questions!!!