Current Search:  social network analysis (x)

View All Items

  • CSV Spreadsheet
(1 - 7 of 7)
SPARSIFICATION OF SOCIAL NETWORKS USING RANDOM WALKS
Title
SPARSIFICATION OF SOCIAL NETWORKS USING RANDOM WALKS.
Creator
Wilder, Bryan, Sukthankar, Gita, University of Central Florida
Abstract / Description
Analysis of large network datasets has become increasingly important. Algorithms have been designed to find many kinds of structure, with numerous applications across the social and biological sciences. However, a tradeoff is always present between accuracy and scalability; otherwise promising techniques can be computationally infeasible when applied to networks with huge numbers of nodes and edges. One way of extending the reach of network analysis is to sparsify the graph by retaining only...
Show moreAnalysis of large network datasets has become increasingly important. Algorithms have been designed to find many kinds of structure, with numerous applications across the social and biological sciences. However, a tradeoff is always present between accuracy and scalability; otherwise promising techniques can be computationally infeasible when applied to networks with huge numbers of nodes and edges. One way of extending the reach of network analysis is to sparsify the graph by retaining only a subset of its edges. The reduced network could prove much more tractable. For this thesis, I propose a new sparsification algorithm that preserves the properties of a random walk on the network. Specifically, the algorithm finds a subset of edges that best preserves the stationary distribution of a random walk by minimizing the Kullback-Leibler divergence between a walk on the original and sparsified graphs. A highly efficient greedy search strategy is developed to optimize this objective. Experimental results are presented that test the performance of the algorithm on the influence maximization task. These results demonstrate that sparsification allows near-optimal solutions to be found in a small fraction of the runtime that would required using the full network. Two cases are shown where sparsification allows an influence maximization algorithm to be applied to a dataset that previous work had considered intractable.
Show less
Date Issued
2015
Identifier
CFH0004732, ucf:45387
Format
Document (PDF)
PURL
http://purl.flvc.org/ucf/fd/CFH0004732
A HYBRID SIMULATION METHODOLOGY TO EVALUATE NETWORK CENTRICDECISION MAKING UNDER EXTREME EVENTS
Title
A HYBRID SIMULATION METHODOLOGY TO EVALUATE NETWORK CENTRICDECISION MAKING UNDER EXTREME EVENTS.
Creator
Quijada, Sergio, Sepulveda, Jose, University of Central Florida
Abstract / Description
Currently the network centric operation and network centric warfare have generated a new area of research focused on determining how hierarchical organizations composed by human beings and machines make decisions over collaborative environments. One of the most stressful scenarios for these kinds of organizations is the so-called extreme events. This dissertation provides a hybrid simulation methodology based on classical simulation paradigms combined with social network analysis for...
Show moreCurrently the network centric operation and network centric warfare have generated a new area of research focused on determining how hierarchical organizations composed by human beings and machines make decisions over collaborative environments. One of the most stressful scenarios for these kinds of organizations is the so-called extreme events. This dissertation provides a hybrid simulation methodology based on classical simulation paradigms combined with social network analysis for evaluating and improving the organizational structures and procedures, mainly the incident command systems and plans for facing those extreme events. According to this, we provide a methodology for generating hypotheses and afterwards testing organizational procedures either in real training systems or simulation models with validated data. As long as the organization changes their dyadic relationships dynamically over time, we propose to capture the longitudinal digraph in time and analyze it by means of its adjacency matrix. Thus, by using an object oriented approach, three domains are proposed for better understanding the performance and the surrounding environment of an emergency management organization. System dynamics is used for modeling the critical infrastructure linked to the warning alerts of a given organization at federal, state and local levels. Discrete simulations based on the defined concept of "community of state" enables us to control the complete model. Discrete event simulation allows us to create entities that represent the data and resource flows within the organization. We propose that cognitive models might well be suited in our methodology. For instance, we show how the team performance decays in time, according to the Yerkes-Dodson curve, affecting the measures of performance of the whole organizational system. Accordingly we suggest that the hybrid model could be applied to other types of organizations, such as military peacekeeping operations and joint task forces. Along with providing insight about organizations, the methodology supports the analysis of the "after action review" (AAR), based on collection of data obtained from the command and control systems or the so-called training scenarios. Furthermore, a rich set of mathematical measures arises from the hybrid models such as triad census, dyad census, eigenvalues, utilization, feedback loops, etc., which provides a strong foundation for studying an emergency management organization. Future research will be necessary for analyzing real data and validating the proposed methodology.
Show less
Date Issued
2006
Identifier
CFE0001243, ucf:46926
Format
Document (PDF)
PURL
http://purl.flvc.org/ucf/fd/CFE0001243
Human Group Behavior Modeling for Virtual Worlds
Title
Human Group Behavior Modeling for Virtual Worlds.
Creator
Shah, Syed Fahad Allam, Sukthankar, Gita, Georgiopoulos, Michael, Foroosh, Hassan, Anagnostopoulos, Georgios, University of Central Florida
Abstract / Description
Virtual worlds and massively-multiplayer online games are rich sources of information about large-scale teams and groups, offering the tantalizing possibility of harvesting data about group formation, social networks, and network evolution. They provide new outlets for human social interaction that differ from both face-to-face interactions and non-physically-embodied social networking tools such as Facebook and Twitter. We aim to study group dynamics in these virtual worlds by collecting and...
Show moreVirtual worlds and massively-multiplayer online games are rich sources of information about large-scale teams and groups, offering the tantalizing possibility of harvesting data about group formation, social networks, and network evolution. They provide new outlets for human social interaction that differ from both face-to-face interactions and non-physically-embodied social networking tools such as Facebook and Twitter. We aim to study group dynamics in these virtual worlds by collecting and analyzing public conversational patterns of users grouped in close physical proximity. To do this, we created a set of tools for monitoring, partitioning, and analyzing unstructured conversations between changing groups of participants in Second Life, a massively multi-player online user-constructed environment that allows users to construct and inhabit their own 3D world. Although there are some cues in the dialog, determining social interactions from unstructured chat data alone is a difficult problem, since these environments lack many of the cues that facilitate natural language processing in other conversational settings and different types of social media. Public chat data often features players who speak simultaneously, use jargon and emoticons, and only erratically adhere to conversational norms.Humans are adept social animals capable of identifying friendship groups from a combination of linguistic cues and social network patterns. But what is more important, the content of what people say or their history of social interactions? Moreover, is it possible to identify whether people are part of a group with changing membership merely from general network properties, such as measures of centrality and latent communities? These are the questions that we aim to answer in this thesis. The contributions of this thesis include: 1) a link prediction algorithm for identifying friendship relationships from unstructured chat data 2) a method for identifying social groups based on the results of community detection and topic analysis.The output of these two algorithms (links and group membership) are useful for studying a variety of research questions about human behavior in virtual worlds. To demonstrate this we have performed a longitudinal analysis of human groups in different regions of the Second Life virtual world. We believe that studies performed with our tools in virtual worlds will be a useful stepping stone toward creating a rich computational model of human group dynamics.
Show less
Date Issued
2011
Identifier
CFE0004164, ucf:49074
Format
Document (PDF)
PURL
http://purl.flvc.org/ucf/fd/CFE0004164
THE IMPACT OF INTRAORGANIZATIONAL TRUST AND LEARNING ORIENTED CLIMATE ON ERROR REPORTING
Title
THE IMPACT OF INTRAORGANIZATIONAL TRUST AND LEARNING ORIENTED CLIMATE ON ERROR REPORTING.
Creator
Sims, Dana Elizabeth, Salas, Eduardo, University of Central Florida
Abstract / Description
Insight into opportunities for process improvement provides a competitive advantage through increases in organizational effectiveness and innovation As a result, it is important to understand the conditions under which employees are willing to communicate this information. This study examined the relationship between trust and psychological safety on the willingness to report errors in a medical setting. Trust and psychological safety were measured at the team and leader level. In addition,...
Show moreInsight into opportunities for process improvement provides a competitive advantage through increases in organizational effectiveness and innovation As a result, it is important to understand the conditions under which employees are willing to communicate this information. This study examined the relationship between trust and psychological safety on the willingness to report errors in a medical setting. Trust and psychological safety were measured at the team and leader level. In addition, the moderating effect of a learning orientation climate at three levels of the organization (i.e., team members, team leaders, organizational) was examined on the relationship between trust and psychological safety on willingness to report errors. Traditional surveys and social network analysis were employed to test the research hypotheses. Findings indicate that team trust, when examined using traditional surveys, is not significantly associated with informally reporting errors. However, when the social networks within the team were examined, evidence that team trust is associated with informally discussing errors was found. Results also indicate that trust in leadership is associated with informally discussing errors, especially severe errors. These findings were supported and expanded to include a willingness to report all severity of errors when social network data was explored. Psychological safety, whether within the team or fostered by leadership, was not found to be associated with a willingness to informally report errors. Finally, learning orientation was not found to be a moderating variable between trust and psychological safety on a willingness to report errors. Instead, organizational learning orientation was found to have a main effect on formally reporting errors to risk management and documenting errors in patient charts. Theoretical and practical implications of the study are offered.
Show less
Date Issued
2009
Identifier
CFE0002818, ucf:48050
Format
Document (PDF)
PURL
http://purl.flvc.org/ucf/fd/CFE0002818
Group Composition Characteristics as Predictors of Shared Leadership: An Exploration of Competing Models of Shared Leadership Emergence
Title
Group Composition Characteristics as Predictors of Shared Leadership: An Exploration of Competing Models of Shared Leadership Emergence.
Creator
Currie, Richard, Ehrhart, Mark, Burke, Shawn, Jex, Steve, University of Central Florida
Abstract / Description
The study of leadership in organizations has received much research attention over the past several decades. However, most of this research has examined hierarchical structures of leadership wherein one individual leads, or is perceived to lead, several other individuals. With a growing number of organizations structuring employees within teams or work groups, researchers have begun studying the ways in which leadership operates in groups. One alternative to the traditional hierarchical...
Show moreThe study of leadership in organizations has received much research attention over the past several decades. However, most of this research has examined hierarchical structures of leadership wherein one individual leads, or is perceived to lead, several other individuals. With a growing number of organizations structuring employees within teams or work groups, researchers have begun studying the ways in which leadership operates in groups. One alternative to the traditional hierarchical structure is for leadership to be distributed or shared in groups such that multiple group members contribute to the overall leadership function of the group. As a result, researchers have begun examining the construct of shared leadership, which is defined as the extent to which multiple group members share in the leadership function of the group. Because shared leadership is a relatively new concept in the research literature, our knowledge of the antecedents of shared leadership is limited. The primary aim of the present research was to explore group composition as a potential antecedent of shared leadership in teams. Group composition was examined in terms of the agreeableness, extraversion, collectivistic work orientation, and trait competitiveness within the group. Mean, minimum/maximum, and variance models of group composition were employed in the present research. A sample of 385 participants comprised a total of 97 groups of three to six individuals to complete a leaderless group discussion exercise and completed measures of shared leadership after completing the group exercise. Results from a series of hierarchical linear regression analyses found no significant relationships between any of predictors and shared leadership using either a social network analysis or a referent-shift approach. Given the short amount of time group members worked on the group task, a clear implication of these findings is that shared leadership requires adequate time to manifest in groups.
Show less
Date Issued
2019
Identifier
CFE0007446, ucf:52694
Format
Document (PDF)
PURL
http://purl.flvc.org/ucf/fd/CFE0007446
Learning Dynamic Network Models for Complex Social Systems
Title
Learning Dynamic Network Models for Complex Social Systems.
Creator
Hajibagheri, Alireza, Sukthankar, Gita, Turgut, Damla, Chatterjee, Mainak, Lakkaraju, Kiran, University of Central Florida
Abstract / Description
Human societies are inherently complex and highly dynamic, resulting in rapidly changing social networks, containing multiple types of dyadic interactions. Analyzing these time-varying multiplex networks with approaches developed for static, single layer networks often produces poor results. To address this problem, our approach is to explicitly learn the dynamics of these complex networks. This dissertation focuses on five problems: 1) learning link formation rates; 2) predicting changes in...
Show moreHuman societies are inherently complex and highly dynamic, resulting in rapidly changing social networks, containing multiple types of dyadic interactions. Analyzing these time-varying multiplex networks with approaches developed for static, single layer networks often produces poor results. To address this problem, our approach is to explicitly learn the dynamics of these complex networks. This dissertation focuses on five problems: 1) learning link formation rates; 2) predicting changes in community membership; 3) using time series to predict changes in network structure; 4) modeling coevolution patterns across network layers and 5) extracting information from negative layers of a multiplex network.To study these problems, we created a rich dataset extracted from observing social interactions in the massively multiplayer online game Travian. Most online social media platforms are optimized to support a limited range of social interactions, primarily focusing on communication and information sharing. In contrast, relations in massively-multiplayer online games (MMOGs) are often formed during the course of gameplay and evolve as the game progresses. To analyze the players' behavior, we constructed multiplex networks with link types for raid, communication, and trading.The contributions of this dissertation include 1) extensive experiments on the dynamics of networks formed from diverse social processes; 2) new game theoretic models for community detection in dynamic networks; 3) supervised and unsupervised methods for link prediction in multiplex coevolving networks for both positive and negative links. We demonstrate that our holistic approach for modeling network dynamics in coevolving, multiplex networks outperforms factored methods that separately consider temporal and cross-layer patterns.
Show less
Date Issued
2017
Identifier
CFE0006598, ucf:51306
Format
Document (PDF)
PURL
http://purl.flvc.org/ucf/fd/CFE0006598
Settlement History and Interaction in the Manialtepec Basin of Oaxaca's Central Coast
Title
Settlement History and Interaction in the Manialtepec Basin of Oaxaca's Central Coast.
Creator
Menchaca, Victoria, Barber, Sarah, Walker, John, Chase, Arlen, University of Central Florida
Abstract / Description
As the focus of over 70 years' of archaeological research, Oaxaca, Mexico, is one of Mesoamerica's best understood regions. Yet, despite the volume of work in Oaxaca, information about one of its key resource areas, the central Pacific coast, remains limited. Specifically, the ambiguous role of Oaxaca's Central Coast in interregional relationships during pre-Hispanic times to the sites of Monte Alb(&)#225;n and Tututepec has been a chronic problem and major source of debate for decades. The...
Show moreAs the focus of over 70 years' of archaeological research, Oaxaca, Mexico, is one of Mesoamerica's best understood regions. Yet, despite the volume of work in Oaxaca, information about one of its key resource areas, the central Pacific coast, remains limited. Specifically, the ambiguous role of Oaxaca's Central Coast in interregional relationships during pre-Hispanic times to the sites of Monte Alb(&)#225;n and Tututepec has been a chronic problem and major source of debate for decades. The purpose of this thesis is to begin clarifying the role of Oaxaca's Central Coast in interregional networks and its pre-Hispanic history. Analysis utilized surface observations, surface collections, and information from limited excavations performed by the Proyecto Arqueol(&)#243;gico Laguna de Manialtepec (PALM) in the Manialtepec Basin, located on the Central Coast of Oaxaca. The data was then mapped using ArcGIS software to render settlement and artifact patterns. Based on the results of this project I suggest a history of settlement for this area. I also argue that the Basin contained three centers, maintained interregional interactions, and was invaded by the Mixtecs of highland Oaxaca during the Late Postclassic Period (A.D. 1200-1500).
Show less
Date Issued
2015
Identifier
CFE0005843, ucf:50920
Format
Document (PDF)
PURL
http://purl.flvc.org/ucf/fd/CFE0005843