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21
Animating the Development of Social Networks over Time using a Dynamic Extension of Multidimensional Scaling. El Profesional de la Información
, 2008
"... The animation of network visualizations poses technical and theoretical challenges. Rather stable patterns are required before the mental map enables a user to make inferences over time. In order to enhance stability, we developed an extension of stressminimization with developments over time. This ..."
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Cited by 9 (6 self)
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The animation of network visualizations poses technical and theoretical challenges. Rather stable patterns are required before the mental map enables a user to make inferences over time. In order to enhance stability, we developed an extension of stressminimization with developments over time. This dynamic layouter is no longer based on linear interpolation between independent static visualizations, but change over time is used as a parameter in the optimization. Because of our focus on structural change versus stability the attention is shifted from the relational graph to the latent eigenvectors of matrices. The approach is illustrated with animations for the journal citation environments of Social Networks, the (co-)author networks in the carrying community of this journal, and the topical development using relations among its title words. Our results are also compared with animations based on PajekToSVGAnim and SoNIA.
Analyzing Affiliation Networks
- In
, 2011
"... In social network analysis, the term “affiliations ” usually refers to membership or participation data, such as when we have data on which actors have participated in which events. Often, the assumption is that comembership in groups or events is an indicator of an underlying social tie. For exampl ..."
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Cited by 6 (2 self)
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In social network analysis, the term “affiliations ” usually refers to membership or participation data, such as when we have data on which actors have participated in which events. Often, the assumption is that comembership in groups or events is an indicator of an underlying social tie. For example, Davis Gardner and Gardner (1941) used data provided by the society pages of a local newspaper to uncover distinct social circles among a set of society women. Similarly, Domhoff (1967) and others have used comembership in corporate boards to search for social elites (e.g., Allen, 1974; Carroll, Fox and Ornstein, 1982; Galaskiewicz, 1985; Westphal and Khanna, 2003). Alternatively, we can see co-participation as providing opportunities for social ties to develop, which in turn provide opportunities things like ideas to flow between actors. For example, Davis (1991; Davis and Greeve, 1997) studied the diffusion of corporate practices such as poison pills and golden parachutes. He finds evidence that poison pills diffuse through chains of interlocking directorates, where board members who sit on multiple boards serve as conduits of strategic information between the different firms. An important advantage of affiliation data, especially in the case studying elites, is that affiliations are often observable from a distance (e.g., government records, newspaper reports), without having to have special access to the actors. In this chapter, we focus on issues involving the analysis of affiliation data, as opposed to the collection
Role Discovery in Networks
, 2014
"... Roles represent node-level connectivity patterns such as star-center, star-edge nodes, near-cliques or nodes that act as bridges to different regions of the graph. Intuitively, two nodes belong to the same role if they are stru-turally similar. Roles have been mainly of interest to sociologists, b ..."
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Cited by 4 (2 self)
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Roles represent node-level connectivity patterns such as star-center, star-edge nodes, near-cliques or nodes that act as bridges to different regions of the graph. Intuitively, two nodes belong to the same role if they are stru-turally similar. Roles have been mainly of interest to sociologists, but more recently, roles have become increasingly useful in other domains. Traditionally, the notion of roles were defined based on graph equivalences such as structural, regular, and stochastic equivalences. We briefly revisit the notions and instead propose a more general formulation of roles based on the similarity of a feature representation (in contrast to the graph representation). This leads us to propose a taxonomy of two general classes of techniques for discovering roles which includes (i) graph-based roles and (ii) feature-based roles. This survey focuses primarily on feature-based roles. In particular, we also introduce a flexible framework for discovering roles using the notion of structural similarity on a feature-based representation. The framework consists of two fundamental components: (1) role feature construction and (2) role assignment using the learned feature representation. We discuss the relevant decisions for discovering feature-based roles and highlight the advantages and disadvantages of the many techniques that can be used for this purpose. Finally, we discuss potential applications and future directions and challenges.
A Triclustering Approach for Time Evolving Graphs
, 2013
"... Abstract—This paper introduces a novel technique to track structures in time evolving graphs. The method is based on a parameter free approach for three-dimensional co-clustering of the source vertices, the target vertices and the time. All these features are simultaneously segmented in order to bui ..."
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Cited by 2 (2 self)
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Abstract—This paper introduces a novel technique to track structures in time evolving graphs. The method is based on a parameter free approach for three-dimensional co-clustering of the source vertices, the target vertices and the time. All these features are simultaneously segmented in order to build time segments and clusters of vertices whose edge distributions are similar and evolve in the same way over the time segments. The main novelty of this approach lies in that the time segments are directly inferred from the evolution of the edge distribution between the vertices, thus not requiring the user to make an a priori discretization. Experiments conducted on a synthetic dataset illustrate the good behaviour of the technique, and a study of a real-life dataset shows the potential of the proposed approach for exploratory data analysis. Keywords-Coclustering;Blockmodeling;Graph Mining;Model Selection
Community Evolution Mining in Dynamic Social Networks
- PROCEDIA SOCIAL AND BEHAVIORAL SCIENCES 00 (2011) 48–57
, 2011
"... Data that encompasses relationships is represented by a graph of interconnected nodes. Social network analysis is the study of such graphs which examines questions related to structures and patterns that can lead to the understanding of the data and predicting the trends of social networks. Static a ..."
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Cited by 2 (0 self)
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Data that encompasses relationships is represented by a graph of interconnected nodes. Social network analysis is the study of such graphs which examines questions related to structures and patterns that can lead to the understanding of the data and predicting the trends of social networks. Static analysis, where the time of interaction is not considered (i.e., the network is frozen in time), misses the opportunity to capture the evolutionary patterns in dynamic networks. Specifically, detecting the community evolutions, the community structures that changes in time, provides insight into the underlying behaviour of the network. Recently, a number of researchers have started focusing on identifying critical events that characterize the evolution of communities in dynamic scenarios. In this paper, we present a framework for modeling and detecting community evolution in social networks, where a series of significant events is defined for each community. A community matching algorithm is also proposed to efficiently identify and track similar communities over time. We also define the concept of meta community which is a series of similar communities captured in different timeframes and detected by our matching algorithm. We illustrate the capabilities and potential of our framework by applying it to two real datasets. Furthermore, the events detected by the framework is supplemented by extraction and investigation of the topics discovered for each community.
Visualization of Networked Collaboration in Digital Ecosystems through Two-mode Network Patterns
"... Collaboration in Digital Ecosystems can be very complex due to varying types and numbers of actors and artifacts, and the many possible interactions between these entities. Hereby, network visualizations are useful for analyzing networked collaboration and consequently for supporting cognitive proce ..."
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Cited by 1 (0 self)
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Collaboration in Digital Ecosystems can be very complex due to varying types and numbers of actors and artifacts, and the many possible interactions between these entities. Hereby, network visualizations are useful for analyzing networked collaboration and consequently for supporting cognitive processes, like fostering reflection, enabling awareness in students ‟ learning. In this paper, we examine different techniques for visualizing ICT-enabled interactions in Digital Ecosystems. After giving a brief overview of related work, we argue for the application of two-mode networks for visualizing patterns of networked collaboration and discuss different issues by comparing this technique to traditional visualizations. Categories and Subject Descriptors
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"... This paper visualizes the evolution of the dominant hierarchical and regional patterns in the world city network, drawing upon an analytical framework integrating categorical correlation, hierarchical clustering, and alluvial diagrams. Our analysis confirms the continued interweaving of hierarchical ..."
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This paper visualizes the evolution of the dominant hierarchical and regional patterns in the world city network, drawing upon an analytical framework integrating categorical correlation, hierarchical clustering, and alluvial diagrams. Our analysis confirms the continued interweaving of hierarchical and regional patterns in the world city network as measured by cities ’ similarities in the presence of globalized service firms, but equally highlights some of the key changes that have occurred between 2000 and 2010 such as the rise of the BRIC cities, Dubai’s leading positions in the Arab Gulf, and the stratification of US cities.
