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

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

Digital Trace Data massive unstructured granular heterogenous dynamic performative

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

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?

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

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.

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.

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. 

Network Analysis An example network using WordPress data from December 2012. 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.

Heat Map Plugin topology overlap matrix plots across time. Topology overlab matricies for 2011-2014 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.

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

Questions!!!