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- Chordal graphs (4)
- Correlation networks (3)
- Clustering (2)
- Graph theory (2)
- Motif finding (2)
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- Social bots (2)
- Social networks analysis (2)
- Advanced Traveller Information Systems (1)
- Benford law (1)
- Benford’s law (1)
- Bioinformatics (1)
- Biological networks (1)
- Biological properties (1)
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- Bot (Internet) (1)
- Centrality (1)
- Cluster overlap (1)
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- Collaborative learning indicators (1)
- Common method bias (1)
- Computer crime (1)
- Correlation network (1)
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- Digital platforms (1)
- Driver nodes (1)
- Edge enrichment (1)
- Essential genes (1)
Articles 1 - 19 of 19
Full-Text Articles in Physical Sciences and Mathematics
Application Of The Benford’S Law To Social Bots And Information Operations Activities, Lale Madahali, Margeret Hall
Application Of The Benford’S Law To Social Bots And Information Operations Activities, Lale Madahali, Margeret Hall
Interdisciplinary Informatics Faculty Proceedings & Presentations
Benford's law shows the pattern of behavior in normal systems. It states that in natural systems digits' frequency have a certain pattern such that the occurrence of first digits in numbers are unevenly distributed. In systems with natural behavior, numbers begin with a “1” are more common than numbers beginning with “9”. It implies that if the distribution of first digits deviate from the expected distribution, it is indicative of fraud. It has many applications in forensic accounting, stock markets, finding abnormal data in survey data, and natural science. We investigate whether social media bots and Information Operations activities are …
Structural Bot Detection In Social Networks, Lale Madahali
Structural Bot Detection In Social Networks, Lale Madahali
Interdisciplinary Informatics Faculty Proceedings & Presentations
Social network platforms are a major part of toady’s life. They are usually used for entertainment, news, advertisements, and branding for businesses and individuals alike. However, use of automated accounts, also known as bots, pollute this environment and avoid having a reliable clean online world. In this work, I address the problem of detecting bots in online social networks.
Exploratory Factor Analysis Of Graphical Features For Link Prediction In Social Networks, Lale Madahali, Lotfi Najjar, Margeret Hall
Exploratory Factor Analysis Of Graphical Features For Link Prediction In Social Networks, Lale Madahali, Lotfi Najjar, Margeret Hall
Interdisciplinary Informatics Faculty Proceedings & Presentations
Social Networks attract much attention due to their ability to replicate social interactions at scale. Link prediction, or the assessment of which unconnected nodes are likely to connect in the future, is an interesting but non-trivial research area. Three approaches exist to deal with the link prediction problem: feature-based models, Bayesian probabilistic models, probabilistic relational models. In feature-based methods, graphical features are extracted and used for classification. Usually, these features are subdivided into three feature groups based on their formula. Some formulas are extracted based on neighborhood graph traverse. Accordingly, there exists three groups of features, neighborhood features, path-based features, …
Knowing And Designing: Understanding Information Use In Open Source Design Through The Lens Of Information Archetypes, Kevin Lumbard, Ammar Abid, Christine Toh, Matt Germonprez
Knowing And Designing: Understanding Information Use In Open Source Design Through The Lens Of Information Archetypes, Kevin Lumbard, Ammar Abid, Christine Toh, Matt Germonprez
Interdisciplinary Informatics Faculty Proceedings & Presentations
The early phases of the product design process are crucial to the success of design outcomes. While information utilized during idea development has tremendous potential to impact the final design, there is a lack of understanding about the types of information utilized in industry, making it challenging to develop and teach methodologies that support the design of competitive products. As a first step in understanding this process, this study focuses on developing a framework of Information Archetypes utilized by designers in industry. This was accomplished through in-depth analysis of qualitative interviews with large software engineering companies. The results reveal two …
Is Quality Control Pointless?, Markus Karuse, Margeret A. Hall, Simon James Caton
Is Quality Control Pointless?, Markus Karuse, Margeret A. Hall, Simon James Caton
Interdisciplinary Informatics Faculty Proceedings & Presentations
Intrinsic to the transition towards, and necessary for the success of digital platforms as a service (at scale) is the notion of human computation. Going beyond ‘the wisdom of the crowd’, human computation is the engine that powers platforms and services that are now ubiquitous like Duolingo and Wikipedia. In spite of increasing research and population interest, several issues remain open and in debate on large-scale human computation projects. Quality control is first among these discussions. We conducted an experiment with three different tasks of varying complexity and five different methods to distinguish and protect against constantly under-performing contributors. We …
Do We Choose What We Desire? – Persuading Citizens To Make Consistent And Sustainable Mobility Decisions, Christopher Lisson, Margeret A. Hall
Do We Choose What We Desire? – Persuading Citizens To Make Consistent And Sustainable Mobility Decisions, Christopher Lisson, Margeret A. Hall
Interdisciplinary Informatics Faculty Proceedings & Presentations
A dilemma in urban mobility with tremendous effects on citizens’ wellbeing is the unconscious antipode between their short- and long-term goals. People do not anticipate all consequences of their modal choices and thus make decisions that might be incoherent with their desires, e.g. taking their own car due to convenience but causing a congested city. Omnipresent Information Systems on smartphones provide the necessary information and coordination capabilities to support people for sustainable and individually coherent mobility decisions on a mass scale. Building upon extant work in travel behavior and social psychology, a framework is proposed to coordinate research efforts in …
A Crowdsourcing Approach To Identify Common Method Bias And Self-Representation, Margeret A. Hall, Simon Caton
A Crowdsourcing Approach To Identify Common Method Bias And Self-Representation, Margeret A. Hall, Simon Caton
Interdisciplinary Informatics Faculty Proceedings & Presentations
Pertinent questions on the measurement of social indicators are: the verification of data gained online (e.g., controlling for self-representation on social networks), and appropriate uses in community management and policy-making. Across platforms like Facebook, LinkedIn, Twitter, and blogging services, users (sub)consciously represent themselves in a way which is appropriate for their intended audience (Qui et al., 2012; Zhao et al., 2008). However, scholars in the social sciences and computer science have not yet adequately addressed controlling for self-representation, or the propensity to display or censor oneself, in their analyses (Zhao et al., 2008; Das and Kramer, 2013). As such researchers …
Toward Visualization-Specific Heuristic Evaluation, Alvin E. Tarrell, Camilla Forsell, Ann L. Fruhling, Georges Grinstein, Rita Borgo, Jean Scholtz
Toward Visualization-Specific Heuristic Evaluation, Alvin E. Tarrell, Camilla Forsell, Ann L. Fruhling, Georges Grinstein, Rita Borgo, Jean Scholtz
Interdisciplinary Informatics Faculty Proceedings & Presentations
This position paper describes heuristic evaluation as it relates to visualization and visual analytics. We review heuristic evaluation in general, then comment on previous process-based, performance-based, and framework-based efforts to adapt the method to visualization-specific needs. We postulate that the framework-based approach holds the most promise for future progress in development of visualization-specific heuristics, and propose a specific framework as a starting point. We then recommend a method for community involvement and input into the further development of the heuristic framework and more detailed design and evaluation guidelines.
Making Solution Pluralism In Policy Making Accessible: Optimization Of Design And Services For Constituent Well-Being, Margeret A. Hall, Steven O. Kimbrough, Wibke Michalk, Jefff Schneider, Christof Weinhardt
Making Solution Pluralism In Policy Making Accessible: Optimization Of Design And Services For Constituent Well-Being, Margeret A. Hall, Steven O. Kimbrough, Wibke Michalk, Jefff Schneider, Christof Weinhardt
Interdisciplinary Informatics Faculty Proceedings & Presentations
Policy makers are increasingly turning to computational support mechanisms for managing uncertainty, and constituent focused-decisions. Utilization and standardization of human-computer interaction principles to create solution pluralism (the condition of having a consideration set containing a multiplicity of credible solutions) is a fundamental to fulfilling this need. There is a need for standardized applications and user interfaces to deliver a higher quality of service, which assists policy makers in maintaining or increasing constituent well-being.
On Identifying And Analyzing Significant Nodes In Protein-Protein Interaction Networks, Rohan Khazanchi, Kathryn Dempsey Cooper, Ishwor Thapa, Hesham Ali
On Identifying And Analyzing Significant Nodes In Protein-Protein Interaction Networks, Rohan Khazanchi, Kathryn Dempsey Cooper, Ishwor Thapa, Hesham Ali
Interdisciplinary Informatics Faculty Proceedings & Presentations
Network theory has been used for modeling biological data as well as social networks, transportation logistics, business transcripts, and many other types of data sets. Identifying important features/parts of these networks for a multitude of applications is becoming increasingly significant as the need for big data analysis techniques grows. When analyzing a network of protein-protein interactions (PPIs), identifying nodes of significant importance can direct the user toward biologically relevant network features. In this work, we propose that a node of structural importance in a network model can correspond to a biologically vital or significant property. This relationship between topological and …
On Mining Biological Signals Using Correlation Networks, Kathryn Dempsey Cooper, Ishwor Thapa, Claudia Cortes, Zack Eriksen, Dhundy Raj Bastola, Hesham Ali
On Mining Biological Signals Using Correlation Networks, Kathryn Dempsey Cooper, Ishwor Thapa, Claudia Cortes, Zack Eriksen, Dhundy Raj Bastola, Hesham Ali
Interdisciplinary Informatics Faculty Proceedings & Presentations
Correlation networks have been used in biological networks to analyze and model high-throughput biological data, such as gene expression from microarray or RNA-seq assays. Typically in biological network modeling, structures can be mined from these networks that represent biological functions; for example, a cluster of proteins in an interactome can represent a protein complex. In correlation networks built from high-throughput gene expression data, it has often been speculated or even assumed that clusters represent sets of genes that are coregulated. This research aims to validate this concept using network systems biology and data mining by identification of correlation network clusters …
A Novel Multithreaded Algorithm For Extracting Maximal Chordal Subgraphs, Mahantesh Halappanavar, John Feo, Kathryn Dempsey Cooper, Hesham Ali, Sanjukta Bhowmick
A Novel Multithreaded Algorithm For Extracting Maximal Chordal Subgraphs, Mahantesh Halappanavar, John Feo, Kathryn Dempsey Cooper, Hesham Ali, Sanjukta Bhowmick
Interdisciplinary Informatics Faculty Proceedings & Presentations
Chordal graphs are triangulated graphs where any cycle larger than three is bisected by a chord. Many combinatorial optimization problems such as computing the size of the maximum clique and the chromatic number are NP-hard on general graphs but have polynomial time solutions on chordal graphs. In this paper, we present a novel multithreaded algorithm to extract a maximal chordal sub graph from a general graph. We develop an iterative approach where each thread can asynchronously update a subset of edges that are dynamically assigned to it per iteration and implement our algorithm on two different multithreaded architectures - Cray …
On The Design Of Advanced Filters For Biological Networks Using Graph Theoretic Properties, Kathryn Dempsey Cooper, Tzu-Yi Chen, Sanjukta Bhowmick, Hesham Ali
On The Design Of Advanced Filters For Biological Networks Using Graph Theoretic Properties, Kathryn Dempsey Cooper, Tzu-Yi Chen, Sanjukta Bhowmick, Hesham Ali
Interdisciplinary Informatics Faculty Proceedings & Presentations
Network modeling of biological systems is a powerful tool for analysis of high-throughput datasets by computational systems biologists. Integration of networks to form a heterogeneous model requires that each network be as noise-free as possible while still containing relevant biological information. In earlier work, we have shown that the graph theoretic properties of gene correlation networks can be used to highlight and maintain important structures such as high degree nodes, clusters, and critical links between sparse network branches while reducing noise. In this paper, we propose the design of advanced network filters using structurally related graph theoretic properties. While spanning …
The Development Of Parallel Adaptive Sampling Algorithms For Analyzing Biological Networks, Kathryn Dempsey Cooper, Kanimathi Duraisamy, Sanjukta Bhowmick, Hesham Ali
The Development Of Parallel Adaptive Sampling Algorithms For Analyzing Biological Networks, Kathryn Dempsey Cooper, Kanimathi Duraisamy, Sanjukta Bhowmick, Hesham Ali
Interdisciplinary Informatics Faculty Proceedings & Presentations
The availability of biological data in massive scales continues to represent unlimited opportunities as well as great challenges in bioinformatics research. Developing innovative data mining techniques and efficient parallel computational methods to implement them will be crucial in extracting useful knowledge from this raw unprocessed data, such as in discovering significant cellular subsystems from gene correlation networks. In this paper, we present a scalable combinatorial sampling technique, based on identifying maximum chordal subgraphs, that reduces noise from biological correlation networks, thereby making it possible to find biologically relevant clusters from the filtered network. We show how selecting the appropriate filter …
Collaborative Learning In Software Development Teams, Matthew Hale, Rose Gamble, Kimberly Wilson, Anupama Narayan
Collaborative Learning In Software Development Teams, Matthew Hale, Rose Gamble, Kimberly Wilson, Anupama Narayan
Interdisciplinary Informatics Faculty Proceedings & Presentations
Recently Web 2.0 has emerged as a framework to study collaborative learning. Assessing learning in team projects is one mechanism used to improve teaching methodologies and tool support. Web 2.0 technologies enable automated assessment capabilities, leading to both rapid and incremental feedback. Such feedback can catch problems in time for pedagogic adjustment, to better guide students toward reaching learning objectives. Our courseware, SEREBRO, couples a social, tagging enabled, idea network with a range of modular toolkits, such as wikis, feeds and project management tools into a Web 2.0 environment for collaborating teams. In this paper, we first refine a set …
A Novel Correlation Networks Approach For The Identification Of Gene Targets, Kathryn Dempsey Cooper, Stephen Bonasera, Dhundy Raj Bastola, Hesham Ali
A Novel Correlation Networks Approach For The Identification Of Gene Targets, Kathryn Dempsey Cooper, Stephen Bonasera, Dhundy Raj Bastola, Hesham Ali
Interdisciplinary Informatics Faculty Proceedings & Presentations
Correlation networks are emerging as a powerful tool for modeling temporal mechanisms within the cell. Particularly useful in examining coexpression within microarray data, studies have determined that correlation networks follow a power law degree distribution and thus manifest properties such as the existence of “hub” nodes and semicliques that potentially correspond to critical cellular structures. Difficulty lies in filtering coincidental relationships from causative structures in these large, noise-heavy networks. As such, computational expenses and algorithm availability limit accurate comparison, making it difficult to identify changes between networks. In this vein, we present our work identifying temporal relationships from microarray data …
Evaluation Of Essential Genes In Correlation Networks Using Measures Of Centrality, Kathryn Dempsey Cooper, Hesham Ali
Evaluation Of Essential Genes In Correlation Networks Using Measures Of Centrality, Kathryn Dempsey Cooper, Hesham Ali
Interdisciplinary Informatics Faculty Proceedings & Presentations
Correlation networks are emerging as powerful tools for modeling relationships in high-throughput data such as gene expression. Other types of biological networks, such as protein-protein interaction networks, are popular targets of study in network theory, and previous analysis has revealed that network structures identified using graph theoretic techniques often relate to certain biological functions. Structures such as highly connected nodes and groups of nodes have been found to correspond to essential genes and protein complexes, respectively. The correlation network, which measures the level of co-variation of gene expression levels, shares some structural properties with other types of biological networks. We …
A Noise Reducing Sampling Approach For Uncovering Critical Properties In Large Scale Biological Networks, Karthik Duraisamy, Kathryn Dempsey Cooper, Hesham Ali, Sanjukta Bhowmick
A Noise Reducing Sampling Approach For Uncovering Critical Properties In Large Scale Biological Networks, Karthik Duraisamy, Kathryn Dempsey Cooper, Hesham Ali, Sanjukta Bhowmick
Interdisciplinary Informatics Faculty Proceedings & Presentations
A correlation network is a graph-based representation of relationships among genes or gene products, such as proteins. The advent of high-throughput bioinformatics has resulted in the generation of volumes of data that require sophisticated in silico models, such as the correlation network, for in-depth analysis. Each element in our network represents expression levels of multiple samples of one gene and an edge connecting two nodes reflects the correlation level between the two corresponding genes in the network according to the Pearson correlation coefficient. Biological networks made in this manner are generally found to adhere to a scale-free structural nature, that …
An Intelligent Data-Centric Approach Toward Identification Of Conserved Motifs In Protein Sequences, Kathryn Dempsey Cooper, Benjamin Currall, Richard Hallworth, Hesham Ali
An Intelligent Data-Centric Approach Toward Identification Of Conserved Motifs In Protein Sequences, Kathryn Dempsey Cooper, Benjamin Currall, Richard Hallworth, Hesham Ali
Interdisciplinary Informatics Faculty Proceedings & Presentations
The continued integration of the computational and biological sciences has revolutionized genomic and proteomic studies. However, efficient collaboration between these fields requires the creation of shared standards. A common problem arises when biological input does not properly fit the expectations of the algorithm, which can result in misinterpretation of the output. This potential confounding of input/output is a drawback especially when regarding motif finding software. Here we propose a method for improving output by selecting input based upon evolutionary distance, domain architecture, and known function. This method improved detection of both known and unknown motifs in two separate case studies. …