Dynamic Structure Mining

Limitations to Traditional SNA Social network analysis (SNA) has focused on small, bounded networks, with 2-3 types of links (such as friendship and advice) among one type of node (such as people), at one point in time, with close to perfect information. While it is understood, at least in principle how to think about multi- modal, multi-plex, dynamic networks, the number of tools, the interpretation of the measures, and the illustrative studies using such “higher order” networks are still in their infancy relative to what is available for simpler networks.

Dynamic Structure Mining New nodes may be added to the system and old nodes may be removed. New links may emerge between originally disconnected nodes and old links may rewire or break. Understanding the dynamics and the process of evolution in networks is of vital practical importance. There are two general research questions in this area: (a) How to describe the dynamics? and (b) How to model and predict the dynamics?

Dynamic Structure Mining Recently there have been a number of advances that extend SNA to the realm of dynamic analysis and multi-color networks. There are three key advances: 1) the metamatrix, 2) treating ties as probabilistic, and 3) combining social networks with cognitive science and multi-agent systems. These advances result in a dynamic network analysis.

Research on network dynamics Mathematics: (purely) random graphs (Erd¨os - Renyi) Computer science, theoretical physics: study of effects of simple, but not completely random, rules in infinitely growing networks (e.g., www) Sociology: interaction between individuals (friendship, collaboration, sex) but also other social actors – organizations, countries. Economics: choices by individuals with interdependent payoffs game theory – strategic behavior; equilibrium, stable networks. Biology: interacting species, interacting proteins.

Statistical (inferential) modeling of networks allows to test theories about network development, about mutually interdependent development of networks and behavior; allows generalization from empirical data to conclusions about populations.

Methodological research program how to model network dynamics how to model joint networks & behavior dynamics how robust are conclusions to misspecification work in progress — leads to new perspectives on old questions, and to new questions, new wishes for data collection; requires painstaking modeling, collaboration methodologists – social scientists. Misspecification is an issue that has not yet been addressed sufficiently.

Meta-Matrix Focus on people, knowledge/resources, events/tasks and organizations. A core issue for DNA is what are the appropriate metrics for describing and contrasting dynamic networks.

Meta-Matrix

Probabilistic Ties The ties in the meta-matrix are probabilistic. Various factors affect the probability, including the observer’s certainty in the tie and the likelihood that the tie is manifest at that time. Bayesian updating techniques (Dombroski and Carley, 2002), cognitive inferencing techniques, and models of social and cognitive change processes (Carley, 2002; Carley, Lee and Krackhardt, 2001) can be used to estimate the probability and how it changes over time.

Multi-Agent Network Models A major problem with traditional SNA is that the people in the networks are not treated as active adaptive agents capable of taking action, learning, and altering their networks. Multi-agent technology in which the agents use these mechanisms, learn, take part in events, do tasks to model organizational and social change. The dynamic social network emerges from these actions.

Dynamic Network Theory How do networks change? What are the basic processes?

Addition and Removal of Relations Basic processes are cognitive, social and political in nature. Cognitive processes have to do with learning and forgetting, the changes that occur in ties due to changes in what individuals know. Social changes occur when one agent or organization dictates a change in ties, such as when a manager re-assigns individuals to tasks. Political changes are due to legislation that effect organizations and the over-arching goals.

Change Process for Relations in the Meta-Matrix

Tools AutoMap, ORA, and Construct Data extraction and cleaning can be done for raw text by using a combination of AutoMap and ORA. Analysis and forecasting of change in networks can be accomplished by using ORA and Construct. The tools described here support analysis of networks ranging in size and scope from a few nodes to 106 nodes per ontology class.

AutoMap A mixed-initiative system for the extraction of nodes and relations from raw unformatted texts. Content analysis (extraction of concepts and frequencies), Semantic network analysis (extraction of network of concepts), Dynamic-network analysis (extraction of ontologically cross-classified nodes and relations) Aspects of sentiment analysis.

AutoMap and ORA are used together in a predefined and optimized sequence to clean and structure the extracted meta-network data. In general, the techniques for extracting and classifying agents, organizations, and locations are more accurate than those for knowledge, resources, tasks and beliefs. CEMAP is a mixed-initiative system for the extraction of nodes and relations from semi-structured texts, such as blogs or .

ORA A powerful network analysis tool, capable of handling large 10 6 networks, and supporting meta-network data, geo-spatial network data, and dynamic network data. Relatively unique features include trail, network, and geo-network visualization, classical and fuzzy grouping algorithms, multi-mode network assessment. ORA can import and export data in a large number of formats including direct imports for CSV and UCINET and export of images in png, jpg, pdf, and svg.

Construct An agent-based dynamic-network model for assessing the co- evolution of social and knowledge networks through fundamental learning, information diffusion and belief dispersion processes. Using Construct the impact of various interventions can be assessed at the individual, group or network level under alternative communication media environments. Construct gains its power for evolving change in the networks by accounting for the influence of bi-partite networks in constraining the development of the uni-modal networks.

DyNetML An XML based language for the interchange of relational data. DyNetML is used to exchange data between AutoMap and ORA with AutoMap exporting DyNetML and ORA importing and exporting DyNetML.