164 research outputs found
A Book of Generations – Writing at the Frontier
We address the problem of finding viewpoints that preserve the relational structure of a three-dimensional graph drawing under orthographic parallel projection. Previously, algorithms for finding the best viewpoints under two natural models of viewpoint “goodness” were proposed. Unfortunately, the inherent combinatorial complexity of the problem makes finding exact solutions is impractical. In this paper, we propose two approximation algorithms for the problem, commenting on their design, and presenting results on their performance
Design of chemical space networks incorporating compound distance relationships
Networks, in which nodes represent compounds and edges pairwise similarity relationships, are used as coordinate-free representations of chemical space. So-called chemical space networks (CSNs) provide intuitive access to structural relationships within compound data sets and can be annotated with activity information. However, in such similarity-based networks, distances between compounds are typically determined for layout purposes and clarity and have no chemical meaning. By contrast, inter-compound distances as a measure of dissimilarity can be directly obtained from coordinate-based representations of chemical space. Herein, we introduce a CSN variant that incorporates compound distance relationships and thus further increases the information content of compound networks. The design was facilitated by adapting the Kamada-Kawai algorithm. Kamada-Kawai networks are the first CSNs that are based on numerical similarity measures, but do not depend on chosen similarity threshold values
A statistical network analysis of the HIV/AIDS epidemics in Cuba
The Cuban contact-tracing detection system set up in 1986 allowed the
reconstruction and analysis of the sexual network underlying the epidemic
(5,389 vertices and 4,073 edges, giant component of 2,386 nodes and 3,168
edges), shedding light onto the spread of HIV and the role of contact-tracing.
Clustering based on modularity optimization provides a better visualization and
understanding of the network, in combination with the study of covariates. The
graph has a globally low but heterogeneous density, with clusters of high
intraconnectivity but low interconnectivity. Though descriptive, our results
pave the way for incorporating structure when studying stochastic SIR epidemics
spreading on social networks
A Tool for IT-supported Visualization and Analysis of Virtual Communication Networks in Knowledge Communities
A Regularized Graph Layout Framework for Dynamic Network Visualization
Many real-world networks, including social and information networks, are
dynamic structures that evolve over time. Such dynamic networks are typically
visualized using a sequence of static graph layouts. In addition to providing a
visual representation of the network structure at each time step, the sequence
should preserve the mental map between layouts of consecutive time steps to
allow a human to interpret the temporal evolution of the network. In this
paper, we propose a framework for dynamic network visualization in the on-line
setting where only present and past graph snapshots are available to create the
present layout. The proposed framework creates regularized graph layouts by
augmenting the cost function of a static graph layout algorithm with a grouping
penalty, which discourages nodes from deviating too far from other nodes
belonging to the same group, and a temporal penalty, which discourages large
node movements between consecutive time steps. The penalties increase the
stability of the layout sequence, thus preserving the mental map. We introduce
two dynamic layout algorithms within the proposed framework, namely dynamic
multidimensional scaling (DMDS) and dynamic graph Laplacian layout (DGLL). We
apply these algorithms on several data sets to illustrate the importance of
both grouping and temporal regularization for producing interpretable
visualizations of dynamic networks.Comment: To appear in Data Mining and Knowledge Discovery, supporting material
(animations and MATLAB toolbox) available at
http://tbayes.eecs.umich.edu/xukevin/visualization_dmkd_201
"Meaning" as a sociological concept: A review of the modeling, mapping, and simulation of the communication of knowledge and meaning
The development of discursive knowledge presumes the communication of meaning
as analytically different from the communication of information. Knowledge can
then be considered as a meaning which makes a difference. Whereas the
communication of information is studied in the information sciences and
scientometrics, the communication of meaning has been central to Luhmann's
attempts to make the theory of autopoiesis relevant for sociology. Analytical
techniques such as semantic maps and the simulation of anticipatory systems
enable us to operationalize the distinctions which Luhmann proposed as relevant
to the elaboration of Husserl's "horizons of meaning" in empirical research:
interactions among communications, the organization of meaning in
instantiations, and the self-organization of interhuman communication in terms
of symbolically generalized media such as truth, love, and power. Horizons of
meaning, however, remain uncertain orders of expectations, and one should
caution against reification from the meta-biological perspective of systems
theory
Community evolution in patent networks: technological change and network dynamics
When studying patent data as a way to understand innovation and technological change, the conventional indicators might fall short, and categorizing technologies based on the existing classification systems used by patent authorities could cause inaccuracy and misclassification, as shown in literature. Gao et al. (International Workshop on Complex Networks and their Applications, 2017) have established a method to analyze patent classes of similar technologies as network communities. In this paper, we adopt the stabilized Louvain method for network community detection to improve consistency and stability. Incorporating the overlapping community mapping algorithm, we also develop a new method to identify the central nodes based on the temporal evolution of the network structure and track the changes of communities over time. A case study of Germany’s patent data is used to demonstrate and verify the application of the method and the results. Compared to the non-network metrics and conventional network measures, we offer a heuristic approach with a dynamic view and more stable results
Community evolution in patent networks: technological change and network dynamics
When studying patent data as a way to understand innovation and technological change, the conventional indicators might fall short, and categorizing technologies based on the existing classification systems used by patent authorities could cause inaccuracy and misclassification, as shown in literature. Gao et al. (International Workshop on Complex Networks and their Applications, 2017) have established a method to analyze patent classes of similar technologies as network communities. In this paper, we adopt the stabilized Louvain method for network community detection to improve consistency and stability. Incorporating the overlapping community mapping algorithm, we also develop a new method to identify the central nodes based on the temporal evolution of the network structure and track the changes of communities over time. A case study of Germany’s patent data is used to demonstrate and verify the application of the method and the results. Compared to the non-network metrics and conventional network measures, we offer a heuristic approach with a dynamic view and more stable results
Recognition of Crowd Behavior from Mobile Sensors with Pattern Analysis and Graph Clustering Methods
Mobile on-body sensing has distinct advantages for the analysis and
understanding of crowd dynamics: sensing is not geographically restricted to a
specific instrumented area, mobile phones offer on-body sensing and they are
already deployed on a large scale, and the rich sets of sensors they contain
allows one to characterize the behavior of users through pattern recognition
techniques.
In this paper we present a methodological framework for the machine
recognition of crowd behavior from on-body sensors, such as those in mobile
phones. The recognition of crowd behaviors opens the way to the acquisition of
large-scale datasets for the analysis and understanding of crowd dynamics. It
has also practical safety applications by providing improved crowd situational
awareness in cases of emergency.
The framework comprises: behavioral recognition with the user's mobile
device, pairwise analyses of the activity relatedness of two users, and graph
clustering in order to uncover globally, which users participate in a given
crowd behavior. We illustrate this framework for the identification of groups
of persons walking, using empirically collected data.
We discuss the challenges and research avenues for theoretical and applied
mathematics arising from the mobile sensing of crowd behaviors
The role of endogenous and exogenous mechanisms in the formation of R&D networks
We develop an agent-based model of strategic link formation in Research and Development (R&D)networks. Empirical evidence has shown that the growth of these networks is driven by mechanisms whichare both endogenous to the system (that is, depending on existing alliances patterns) and exogenous (that is, driven by an exploratory search for newcomer firms). Extant research to date has not investigated both mechanisms simultaneously in a comparative manner. To overcome this limitation, we develop a general modeling framework to shed light on the relative importance of these two mechanisms. We test our model against a comprehensive dataset, listing cross-country and cross-sectoral R&D alliances from 1984 to 2009. Our results show that by fitting only three macroscopic properties of the network topology, this framework is able to reproduce a number of micro-level measures, including the distributions of degree, local clustering, path length and component size, and the emergence of network clusters. Furthermore, by estimating the link probabilities towards newcomers and established firms from the data, we find that endogenous mechanisms are predominant over the exogenous ones in the network formation, thus quantifying the importance of existing structures in selecting partner firms
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