Dynamic network analysis

Last updated

Dynamic network analysis (DNA) is an emergent scientific field that brings together traditional social network analysis (SNA), link analysis (LA), social simulation and multi-agent systems (MAS) within network science and network theory. Dynamic networks are a function of time (modeled as a subset of the real numbers) to a set of graphs; for each time point there is a graph. This is akin to the definition of dynamical systems , in which the function is from time to an ambient space, where instead of ambient space time is translated to relationships between pairs of vertices. [1]

Contents

Overview

An example of a multi-entity, multi-network, dynamic network diagram DynamicNetworkAnalysisExample.jpg
An example of a multi-entity, multi-network, dynamic network diagram

There are two aspects of this field. The first is the statistical analysis of DNA data. The second is the utilization of simulation to address issues of network dynamics. DNA networks vary from traditional social networks in that they are larger, dynamic, multi-mode, multi-plex networks, and may contain varying levels of uncertainty. The main difference of DNA to SNA is that DNA takes interactions of social features conditioning structure and behavior of networks into account. DNA is tied to temporal analysis but temporal analysis is not necessarily tied to DNA, as changes in networks sometimes result from external factors which are independent of social features found in networks. One of the most notable and earliest of cases in the use of DNA is in Sampson's monastery study, where he took snapshots of the same network from different intervals and observed and analyzed the evolution of the network. [2]

DNA statistical tools are generally optimized for large-scale networks and admit the analysis of multiple networks simultaneously in which, there are multiple types of nodes (multi-node) and multiple types of links (multi-plex). Multi-node multi-plex networks are generally referred to as meta-networks or high-dimensional networks. In contrast, SNA statistical tools focus on single or at most two mode data and facilitate the analysis of only one type of link at a time.

DNA statistical tools tend to provide more measures to the user, because they have measures that use data drawn from multiple networks simultaneously. Latent space models (Sarkar and Moore, 2005) [3] and agent-based simulation are often used to examine dynamic social networks (Carley et al., 2009). [4] From a computer simulation perspective, nodes in DNA are like atoms in quantum theory, nodes can be, though need not be, treated as probabilistic. Whereas nodes in a traditional SNA model are static, nodes in a DNA model have the ability to learn. Properties change over time; nodes can adapt: A company's employees can learn new skills and increase their value to the network; or, capture one terrorist and three more are forced to improvise. Change propagates from one node to the next and so on. DNA adds the element of a network's evolution and considers the circumstances under which change is likely to occur.

There are three main features to dynamic network analysis that distinguish it from standard social network analysis. First, rather than just using social networks, DNA looks at meta-networks. Second, agent-based modeling and other forms of simulations are often used to explore how networks evolve and adapt as well as the impact of interventions on those networks. Third, the links in the network are not binary; in fact, in many cases they represent the probability that there is a link.

Dynamic Representation Learning

Complex information about object relationships can be effectively condensed into low-dimensional embeddings in a latent space [5] . Dynamic systems, unlike static ones, involve temporal changes. Differences in learned representations over time in a dynamic system can arise from actual changes or arbitrary alterations that do not affect the metrics in the latent space with the former reflecting on the system's stability and the latter linked to the alignment of embeddings [6] .

In essence, the stability of the system defines its dynamics, while misalignment signifies irrelevant changes in the latent space. Dynamic embeddings are considered aligned when variations between embeddings at different times accurately represent the system's actual changes, not meaningless alterations in the latent space. The matter of stability and alignment of dynamic embeddings holds significant importance in various tasks reliant on temporal changes within the latent space. These tasks encompass future metadata prediction, temporal evolution, dynamic visualization, and obtaining average embeddings, among others.

Meta-network

A meta-network is a multi-mode, multi-link, multi-level network. Multi-mode means that there are many types of nodes; e.g., nodes people and locations. Multi-link means that there are many types of links; e.g., friendship and advice. Multi-level means that some nodes may be members of other nodes, such as a network composed of people and organizations and one of the links is who is a member of which organization.

While different researchers use different modes, common modes reflect who, what, when, where, why and how. A simple example of a meta-network is the PCANS formulation with people, tasks, and resources. [7] A more detailed formulation considers people, tasks, resources, knowledge, and organizations. [8] The ORA tool was developed to support meta-network analysis. [9]

Illustrative problems that people in the DNA area work on

See also

Related Research Articles

<span class="mw-page-title-main">Semantic network</span> Knowledge base that represents semantic relations between concepts in a network

A semantic network, or frame network is a knowledge base that represents semantic relations between concepts in a network. This is often used as a form of knowledge representation. It is a directed or undirected graph consisting of vertices, which represent concepts, and edges, which represent semantic relations between concepts, mapping or connecting semantic fields. A semantic network may be instantiated as, for example, a graph database or a concept map. Typical standardized semantic networks are expressed as semantic triples.

DECnet is a suite of network protocols created by Digital Equipment Corporation. Originally released in 1975 in order to connect two PDP-11 minicomputers, it evolved into one of the first peer-to-peer network architectures, thus transforming DEC into a networking powerhouse in the 1980s. Initially built with three layers, it later (1982) evolved into a seven-layer OSI-compliant networking protocol.

<span class="mw-page-title-main">Nonlinear dimensionality reduction</span> Summary of algorithms for nonlinear dimensionality reduction

Nonlinear dimensionality reduction, also known as manifold learning, refers to various related techniques that aim to project high-dimensional data onto lower-dimensional latent manifolds, with the goal of either visualizing the data in the low-dimensional space, or learning the mapping itself. The techniques described below can be understood as generalizations of linear decomposition methods used for dimensionality reduction, such as singular value decomposition and principal component analysis.

<span class="mw-page-title-main">Social network analysis</span> Analysis of social structures using network and graph theory

Social network analysis (SNA) is the process of investigating social structures through the use of networks and graph theory. It characterizes networked structures in terms of nodes and the ties, edges, or links that connect them. Examples of social structures commonly visualized through social network analysis include social media networks, meme spread, information circulation, friendship and acquaintance networks, peer learner networks, business networks, knowledge networks, difficult working relationships, collaboration graphs, kinship, disease transmission, and sexual relationships. These networks are often visualized through sociograms in which nodes are represented as points and ties are represented as lines. These visualizations provide a means of qualitatively assessing networks by varying the visual representation of their nodes and edges to reflect attributes of interest.

Social simulation is a research field that applies computational methods to study issues in the social sciences. The issues explored include problems in computational law, psychology, organizational behavior, sociology, political science, economics, anthropology, geography, engineering, archaeology and linguistics.

Semantic memory refers to general world knowledge that humans have accumulated throughout their lives. This general knowledge is intertwined in experience and dependent on culture. New concepts are learned by applying knowledge learned from things in the past.

<span class="mw-page-title-main">Social complexity</span> Conceptual framework

In sociology, social complexity is a conceptual framework used in the analysis of society. In the sciences, contemporary definitions of complexity are found in systems theory, wherein the phenomenon being studied has many parts and many possible arrangements of the parts; simultaneously, what is complex and what is simple are relative and change in time.

<span class="mw-page-title-main">Computational sociology</span> Branch of the discipline of sociology

Computational sociology is a branch of sociology that uses computationally intensive methods to analyze and model social phenomena. Using computer simulations, artificial intelligence, complex statistical methods, and analytic approaches like social network analysis, computational sociology develops and tests theories of complex social processes through bottom-up modeling of social interactions.

An agent-based model (ABM) is a computational model for simulating the actions and interactions of autonomous agents in order to understand the behavior of a system and what governs its outcomes. It combines elements of game theory, complex systems, emergence, computational sociology, multi-agent systems, and evolutionary programming. Monte Carlo methods are used to understand the stochasticity of these models. Particularly within ecology, ABMs are also called individual-based models (IBMs). A review of recent literature on individual-based models, agent-based models, and multiagent systems shows that ABMs are used in many scientific domains including biology, ecology and social science. Agent-based modeling is related to, but distinct from, the concept of multi-agent systems or multi-agent simulation in that the goal of ABM is to search for explanatory insight into the collective behavior of agents obeying simple rules, typically in natural systems, rather than in designing agents or solving specific practical or engineering problems.

Spatial network analysis software packages are analytic software used to prepare graph-based analysis of spatial networks. They stem from research fields in transportation, architecture, and urban planning. The earliest examples of such software include the work of Garrison (1962), Kansky (1963), Levin (1964), Harary (1969), Rittel (1967), Tabor (1970) and others in the 1960s and 70s. Specific packages address their domain-specific needs, including TransCAD for transportation, GIS for planning and geography, and Axman for Space syntax researchers.

Computer simulation is a prominent method in organizational studies and strategic management. While there are many uses for computer simulation, most academics in the fields of strategic management and organizational studies have used computer simulation to understand how organizations or firms operate. More recently, however, researchers have also started to apply computer simulation to understand organizational behaviour at a more micro-level, focusing on individual and interpersonal cognition and behavior such as team working.

Meta-scheduling or super scheduling is a computer software technique of optimizing computational workloads by combining an organization's multiple job schedulers into a single aggregated view, allowing batch jobs to be directed to the best location for execution.

A wireless ad hoc network (WANET) or mobile ad hoc network (MANET) is a decentralized type of wireless network. The network is ad hoc because it does not rely on a pre-existing infrastructure, such as routers or wireless access points. Instead, each node participates in routing by forwarding data for other nodes. The determination of which nodes forward data is made dynamically on the basis of network connectivity and the routing algorithm in use.

Model-based design (MBD) is a mathematical and visual method of addressing problems associated with designing complex control, signal processing and communication systems. It is used in many motion control, industrial equipment, aerospace, and automotive applications. Model-based design is a methodology applied in designing embedded software.

Evolution in Variable Environment (EVE) is a computer program designed to simulate microbial cellular behavior in various environments. The prediction of cellular responses is a rapidly evolving topic in systems biology and computational biology. The goal is to predict the behavior a particular organism in response to a set of environmental stimuli in silico. Such predictions can have a significant impact on preventive medicine, biotechnology, and microbe re-engineering. Computational prediction of behavior has two major components: the integration and simulation of vast biological networks and the creation of external stimuli. Current limitations of the method are: lack of comprehensive experimental data on the various cellular subsystems and inadequate computational algorithms.

MLDesigner is an integrated modeling and simulation tool for the design and analysis of complex embedded and networked systems. MLDesigner speeds up modeling, simulation and analysis of discrete event, discrete time and continuous time systems concerning architecture, function and performance. The tools is based on ideas of the "Ptolemy Project", done at the University if California Berkeley. MLDesigner is developed by MLDesign Technologies Inc. Palo Alto, CA, USA in collaboration with Mission Level Design GmbH, Ilmenau, Germany.

<span class="mw-page-title-main">OpenComRTOS</span> Real-time operating system

OpenComRTOS is a commercial network-centric, formally developed real-time operating system (RTOS), aimed mainly at the embedded system market.

<span class="mw-page-title-main">Kathleen Carley</span> American social scientist

Kathleen M. Carley is an American computational social scientist specializing in dynamic network analysis. She is a professor in the School of Computer Science in the Carnegie Mellon Institute for Software Research at Carnegie Mellon University and also holds appointments in the Tepper School of Business, the Heinz College, the Department of Engineering and Public Policy, and the Department of Social and Decision Sciences.

<span class="mw-page-title-main">Temporal network</span> Network whose links change over time

A temporal network, also known as a time-varying network, is a network whose links are active only at certain points in time. Each link carries information on when it is active, along with other possible characteristics such as a weight. Time-varying networks are of particular relevance to spreading processes, like the spread of information and disease, since each link is a contact opportunity and the time ordering of contacts is included.

A latent space, also known as a latent feature space or embedding space, is an embedding of a set of items within a manifold in which items resembling each other are positioned closer to one another. Position within the latent space can be viewed as being defined by a set of latent variables that emerge from the resemblances from the objects.

References

  1. ↑ Lotker, Z. (2021). Introduction to Evolving Social Networks. In Analyzing Narratives in Social Networks (pp. 167-185). Springer, Cham.
  2. ↑ Harrison C. White, 1992, Identity and control: A structural theory of social action. Princeton University Press.
  3. ↑ Purnamrita Sarkar and Andrew W. Moore. 2005. Dynamic social network analysis using latent space models. SIGKDD Explor. Newsl. 7, 2 (December 2005), 31-40.
  4. ↑ Kathleen M. Carley, Michael K. Martin and Brian Hirshman, 2009, "The Etiology of Social Change," Topics in Cognitive Science, 1.4:621-650
  5. ↑ Cao, Shaosheng; Lu, Wei; Xu, Qiongkai (2015-10-17). "GraRep: Learning Graph Representations with Global Structural Information". Proceedings of the 24th ACM International on Conference on Information and Knowledge Management. CIKM '15. New York, NY, USA: Association for Computing Machinery: 891–900. doi:10.1145/2806416.2806512. ISBN   978-1-4503-3794-6.
  6. ↑ Gürsoy, Furkan; Haddad, Mounir; Bothorel, Cécile (2023-10-07). "Alignment and stability of embeddings: Measurement and inference improvement". Neurocomputing. 553: 126517. doi:10.1016/j.neucom.2023.126517. ISSN   0925-2312.
  7. ↑ David Krackhardt and Kathleen M. Carley, 1998, "A PCANS Model of Structure in Organization," In proceedings of the 1998 International Symposium on Command and Control Research and Technology, Monterey, CA, June 1998, Evidence Based Research, Vienna, VA, Pp. 113-119.
  8. ↑ Kathleen M. Carley, 2002, "Smart Agents and Organizations of the Future," The Handbook of New Media. Edited by Leah Lievrouw and Sonia Livingstone (Eds.), Thousand Oaks, CA, Sage, Ch. 12: 206-220.
  9. ↑ Kathleen M. Carley. 2014. "ORA: A Toolkit for Dynamic Network Analysis and Visualization," In Reda Alhajj and Jon Rokne (Eds.) Encyclopedia of Social Network Analysis and Mining, Springer.
  10. ↑ Majdandzic, A.; et al. (2013). "Spontaneous recovery in dynamical networks". Nature Physics. 10: 34–38. doi: 10.1038/nphys2819 .
  11. ↑ Michele Starnini, Andrea Baronchelli, Alain Barrat, 2012, Random walks on temporal networks. Phys. Rev. E 85, 056115, http://link.aps.org/doi/10.1103/PhysRevE.85.056115
  12. ↑ René Pfitzner, Ingo Scholtes, Antonios Garas, Claudio Juan Tessone, Frank Schweitzer, 2012, "Betweenness Preference: Quantifying Correlations in the Topological Dynamics of Temporal Networks", Physical Review Letters, Vol. 110, May 10, 2013.
  13. ↑ Carley, Kathleen M., Michael K., Martin and John P. Hancock, 2009, "Dynamic Network Analysis Applied to Experiments from the Decision Architectures Research Environment," Advanced Decision Architectures for the Warfigher: Foundation and Technology, Ch. 4.
  14. ↑ Everton, Sean, 2012, Disrupting Dark Networks, Cambridge University Press, New York, NY
  15. ↑ Kas, Miray, Kathleen M. Carley and L. Richard Carley, 2012, "Who was Where, When? Spatiotemporal Analysis of Researcher Mobility in Nuclear Science," In proceedings of the International Workshop on Spatio Temporal data Integration and Retrieval (STIR 2012), held in conjunction with ICDE 2012, April 1, 2012, Washington D.C.
  16. ↑ Carley, Kathleen. M., Jürgen Pfeffer, Huan Liu, Fred Morstatter, Rebecca Goolsby, 2013, Near Real Time Assessment of Social Media Using Geo-Temporal Network Analytics, In Proceedings of 2013 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM), August 25–28, 2013, Niagara Falls, Canada.
  17. ↑ Merrill, Jacqueline, Mark G. Orr, Christie Y. Jeon, Rosalind V. Wilson, Jonathan Storrick and Kathleen M. Carley, 2012, "Topology of Local Health Officials’ Advice Networks: Mind the Gaps," Journal of Public Health Management Practice, 18(6): 602–608
  18. ↑ Effken, Judith A., Sheila Gephart and Kathleen M. Carley, 2013, "Using ORA to Assess the Relationship of Handoffs to Quality and Safety Outcomes," Computers, Informatics, Nursing. 31(1): 36-44.
  19. ↑ Van Holt, Tracy, Jeffrey C. Johnson, Jamie Brinkley, Kathleen M. Carley and Janna Caspersen, 2012, "Structure of ethnic violence in Sudan: an automated content, meta-network and geospatial analytical approach," Computational and Mathematical Organization Theory, 18:340-355.
  20. ↑ Kenney, Michael J., John Horgan, Cale Horne, Peter Vining, Kathleen M. Carley, Michael Bigrigg, Mia Bloom, Kurt Braddock, 2012, Organizational adaptation in an activist network: Social networks, leadership, and change in al-Muhajiroun, Applied Ergonomics, 44(5):739-747.
  21. ↑ M. Abufouda, K. A. Zweig ."A Theoretical Model for Understanding the Dynamics of Online Social Networks Decay". arXiv preprint arXiv:1610.01538.
  22. ↑ Christian Bokhove, 2016, "Exploring classroom interaction with dynamic social network analysis", International Journal of Research & Method in Education, doi:10.1080/1743727X.2016.1192116.

Further reading

This page is based on this Wikipedia article
Text is available under the CC BY-SA 4.0 license; additional terms may apply.
Images, videos and audio are available under their respective licenses.