2,773 research outputs found
Methods to Determine Node Centrality and Clustering in Graphs with Uncertain Structure
Much of the past work in network analysis has focused on analyzing discrete
graphs, where binary edges represent the "presence" or "absence" of a
relationship. Since traditional network measures (e.g., betweenness centrality)
utilize a discrete link structure, complex systems must be transformed to this
representation in order to investigate network properties. However, in many
domains there may be uncertainty about the relationship structure and any
uncertainty information would be lost in translation to a discrete
representation. Uncertainty may arise in domains where there is moderating link
information that cannot be easily observed, i.e., links become inactive over
time but may not be dropped or observed links may not always corresponds to a
valid relationship. In order to represent and reason with these types of
uncertainty, we move beyond the discrete graph framework and develop social
network measures based on a probabilistic graph representation. More
specifically, we develop measures of path length, betweenness centrality, and
clustering coefficient---one set based on sampling and one based on
probabilistic paths. We evaluate our methods on three real-world networks from
Enron, Facebook, and DBLP, showing that our proposed methods more accurately
capture salient effects without being susceptible to local noise, and that the
resulting analysis produces a better understanding of the graph structure and
the uncertainty resulting from its change over time.Comment: Longer version of paper appearing in Fifth International AAAI
Conference on Weblogs and Social Media. 9 pages, 4 Figure
A similarity-based community detection method with multiple prototype representation
Communities are of great importance for understanding graph structures in
social networks. Some existing community detection algorithms use a single
prototype to represent each group. In real applications, this may not
adequately model the different types of communities and hence limits the
clustering performance on social networks. To address this problem, a
Similarity-based Multi-Prototype (SMP) community detection approach is proposed
in this paper. In SMP, vertices in each community carry various weights to
describe their degree of representativeness. This mechanism enables each
community to be represented by more than one node. The centrality of nodes is
used to calculate prototype weights, while similarity is utilized to guide us
to partitioning the graph. Experimental results on computer generated and
real-world networks clearly show that SMP performs well for detecting
communities. Moreover, the method could provide richer information for the
inner structure of the detected communities with the help of prototype weights
compared with the existing community detection models
Median evidential c-means algorithm and its application to community detection
Median clustering is of great value for partitioning relational data. In this
paper, a new prototype-based clustering method, called Median Evidential
C-Means (MECM), which is an extension of median c-means and median fuzzy
c-means on the theoretical framework of belief functions is proposed. The
median variant relaxes the restriction of a metric space embedding for the
objects but constrains the prototypes to be in the original data set. Due to
these properties, MECM could be applied to graph clustering problems. A
community detection scheme for social networks based on MECM is investigated
and the obtained credal partitions of graphs, which are more refined than crisp
and fuzzy ones, enable us to have a better understanding of the graph
structures. An initial prototype-selection scheme based on evidential
semi-centrality is presented to avoid local premature convergence and an
evidential modularity function is defined to choose the optimal number of
communities. Finally, experiments in synthetic and real data sets illustrate
the performance of MECM and show its difference to other methods
Configuration model for correlation matrices preserving the node strength
Correlation matrices are a major type of multivariate data. To examine
properties of a given correlation matrix, a common practice is to compare the
same quantity between the original correlation matrix and reference correlation
matrices, such as those derived from random matrix theory, that partially
preserve properties of the original matrix. We propose a model to generate such
reference correlation and covariance matrices for the given matrix. Correlation
matrices are often analysed as networks, which are heterogeneous across nodes
in terms of the total connectivity to other nodes for each node. Given this
background, the present algorithm generates random networks that preserve the
expectation of total connectivity of each node to other nodes, akin to
configuration models for conventional networks. Our algorithm is derived from
the maximum entropy principle. We will apply the proposed algorithm to
measurement of clustering coefficients and community detection, both of which
require a null model to assess the statistical significance of the obtained
results.Comment: 8 figures, 4 table
A Comprehensive Bibliometric Analysis on Social Network Anonymization: Current Approaches and Future Directions
In recent decades, social network anonymization has become a crucial research
field due to its pivotal role in preserving users' privacy. However, the high
diversity of approaches introduced in relevant studies poses a challenge to
gaining a profound understanding of the field. In response to this, the current
study presents an exhaustive and well-structured bibliometric analysis of the
social network anonymization field. To begin our research, related studies from
the period of 2007-2022 were collected from the Scopus Database then
pre-processed. Following this, the VOSviewer was used to visualize the network
of authors' keywords. Subsequently, extensive statistical and network analyses
were performed to identify the most prominent keywords and trending topics.
Additionally, the application of co-word analysis through SciMAT and the
Alluvial diagram allowed us to explore the themes of social network
anonymization and scrutinize their evolution over time. These analyses
culminated in an innovative taxonomy of the existing approaches and
anticipation of potential trends in this domain. To the best of our knowledge,
this is the first bibliometric analysis in the social network anonymization
field, which offers a deeper understanding of the current state and an
insightful roadmap for future research in this domain.Comment: 73 pages, 28 figure
Information Flow Structure in Large-Scale Product Development Organizational Networks
In recent years, understanding the structure and function of complex networks has become the foundation for explaining many different real- world complex social, information, biological and technological phenomena. Techniques from statistical physics have been successfully applied to the analysis of these networks, and have uncovered surprising statistical structural properties that have also been shown to have a major effect on their functionality, dynamics, robustness, and fragility. This paper examines, for the first time, the statistical properties of strategically important complex organizational information-based networks -- networks of people engaged in distributed product development -- and discusses the significance of these properties in providing insight into ways of improving the strategic and operational decision-making of the organization. We show that the patterns of information flows that are at the heart of large-scale product development networks have properties that are like those displayed by information, biological and technological networks. We believe that our new analysis methodology and empirical results are also relevant to other organizational information-based human or nonhuman networks.Large-scale product development, socio-technical systems, information systems, social networks, Innovation, complex engineering systems, distributed problem solving
Multilayer Networks
In most natural and engineered systems, a set of entities interact with each
other in complicated patterns that can encompass multiple types of
relationships, change in time, and include other types of complications. Such
systems include multiple subsystems and layers of connectivity, and it is
important to take such "multilayer" features into account to try to improve our
understanding of complex systems. Consequently, it is necessary to generalize
"traditional" network theory by developing (and validating) a framework and
associated tools to study multilayer systems in a comprehensive fashion. The
origins of such efforts date back several decades and arose in multiple
disciplines, and now the study of multilayer networks has become one of the
most important directions in network science. In this paper, we discuss the
history of multilayer networks (and related concepts) and review the exploding
body of work on such networks. To unify the disparate terminology in the large
body of recent work, we discuss a general framework for multilayer networks,
construct a dictionary of terminology to relate the numerous existing concepts
to each other, and provide a thorough discussion that compares, contrasts, and
translates between related notions such as multilayer networks, multiplex
networks, interdependent networks, networks of networks, and many others. We
also survey and discuss existing data sets that can be represented as
multilayer networks. We review attempts to generalize single-layer-network
diagnostics to multilayer networks. We also discuss the rapidly expanding
research on multilayer-network models and notions like community structure,
connected components, tensor decompositions, and various types of dynamical
processes on multilayer networks. We conclude with a summary and an outlook.Comment: Working paper; 59 pages, 8 figure
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