103 research outputs found
Clustering and Community Detection in Directed Networks: A Survey
Networks (or graphs) appear as dominant structures in diverse domains,
including sociology, biology, neuroscience and computer science. In most of the
aforementioned cases graphs are directed - in the sense that there is
directionality on the edges, making the semantics of the edges non symmetric.
An interesting feature that real networks present is the clustering or
community structure property, under which the graph topology is organized into
modules commonly called communities or clusters. The essence here is that nodes
of the same community are highly similar while on the contrary, nodes across
communities present low similarity. Revealing the underlying community
structure of directed complex networks has become a crucial and
interdisciplinary topic with a plethora of applications. Therefore, naturally
there is a recent wealth of research production in the area of mining directed
graphs - with clustering being the primary method and tool for community
detection and evaluation. The goal of this paper is to offer an in-depth review
of the methods presented so far for clustering directed networks along with the
relevant necessary methodological background and also related applications. The
survey commences by offering a concise review of the fundamental concepts and
methodological base on which graph clustering algorithms capitalize on. Then we
present the relevant work along two orthogonal classifications. The first one
is mostly concerned with the methodological principles of the clustering
algorithms, while the second one approaches the methods from the viewpoint
regarding the properties of a good cluster in a directed network. Further, we
present methods and metrics for evaluating graph clustering results,
demonstrate interesting application domains and provide promising future
research directions.Comment: 86 pages, 17 figures. Physics Reports Journal (To Appear
A Network Analysis of Social Balance in Conflict in the Maghreb
This work offers the U.S. military and national security structure a methodology to analyze tension within signed networks based on social balance theory, presents a process to partition a signed network to identify likely subsets within the network, and pinpoints unique actors and relationships based on the structure of the network. Relationships identified to cause increased tension within the network are discovered and analyzed. Identifying this tension provides analysts with insight into the complexities of the network and potential relationships to target to stabilize or destabilize a network. Two Social Network Analysis models have been developed analyzing the relationships of key actors associated with the 2012-2013 conflict in Northern Mali. Relations between the terrorist group Al-Qaida in the Islamic Maghreb (AQIM), several Tuareg organizations, the Malian government and other key actors are assessed, both prior to and immediately following French and other international forces involvement beginning in January 2013. The potential effectiveness of the developed methodology is demonstrated, through the Mali example, in the identification of a specific relationship between two organizations as being under tension to change; subsequently one of the organizations split, reducing the tension and irreversibly changing the network
An Algorithm and Metric for Network Decomposition from Similarity Matrices: Application to Positional Analyses
We present an algorithm for decomposing a social network into an optimal number of structurally equivalent classes. The k-means method is used to determine the best decomposition of the social network for various numbers of subgroups. The best number of subgroups into which to decompose a network is determined by minimizing the intra-cluster variance of similarity subject to the constraint that the improvement in going to more subgroups is better than a random network would achieve. We also describe a decomposability metric that assesses how closely the derived decomposition approaches an ideal network having only structurally equivalent classes.
Three well known network data sets were used to demonstrate the algorithm and decomposability metric. These demonstrations indicate the utility of the approach and suggest how it can be used in a complementary way to the Generalized Blockmodeling
Relevance of Negative Links in Graph Partitioning: A Case Study Using Votes From the European Parliament
In this paper, we want to study the informative value of negative links in
signed complex networks. For this purpose, we extract and analyze a collection
of signed networks representing voting sessions of the European Parliament
(EP). We first process some data collected by the VoteWatch Europe Website for
the whole 7 th term (2009-2014), by considering voting similarities between
Members of the EP to define weighted signed links. We then apply a selection of
community detection algorithms, designed to process only positive links, to
these data. We also apply Parallel Iterative Local Search (Parallel ILS), an
algorithm recently proposed to identify balanced partitions in signed networks.
Our results show that, contrary to the conclusions of a previous study focusing
on other data, the partitions detected by ignoring or considering the negative
links are indeed remarkably different for these networks. The relevance of
negative links for graph partitioning therefore is an open question which
should be further explored.Comment: in 2nd European Network Intelligence Conference (ENIC), Sep 2015,
Karlskrona, Swede
Link Patterns in Complex Networks
Network theorists define patterns in complex networks in various ways to make them accessible to human beholders.
Prominent definitions are thereby based on the partition of the network's nodes into groups such that underlying patterns in the link structure become apparent. Clustering and blockmodeling are two well-known approaches of this kind.
In this thesis, we treat pattern search problems as discrete mathematical optimization problems. From this viewpoint, we develop a new mathematical classification of clustering and blockmodeling approaches, which unifies these two fields and replaces several NP-hardness proofs by a single one.
We furthermore use this classification to develop integer mathematical programming formulations for pattern search problems and discuss new linearization techniques for polynomial functions therein.
We apply these results to a model for a new pattern search problem. Even though it is the most basic problem in combinatorial terms, we can prove its NP-hardness. In fact, we show that it is a generalization of well-known problems including the Traveling Salesman and the Quadratic Assignment Problem. Our derived exact pattern search procedure is up to 10,000 times faster than comparable methods from the literature. To demonstrate its practicability, we finally apply the procedure to the world trade network from the United Nations' database and show that the network deviates by less than 0.14% from the patterns we found
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
Transactional Interactions and Growth in the Global Economy: A Multiple-Network Analysis.
This dissertation postulates that the trend of increasing globalization of economic activities within the shifting features of the world economy has been the dominating force transforming and integrating the international economic structure. I adopt ecological theory\u27s external view of system structure, and focus on a more relational view of international exchange. Using network methodology, I analyze the effects of global transactional interactions on economic growth. This research extends concepts of the macro-urban approach to an analysis of international transactions. Based on data for 93 nations over a 20-year period (1970-1978 and 1978-1990) from three transactional networks (93 x 93 matrices), major effects of international trade, capital, and labor flows on global economic structure and growth are carefully examined. Results indicate that economic interdependencies developed in terms of network positions and changes in the network centralities have been pivotal determinants in reorganizing international economies and creating competitive advantages for economic growth for countries centrally located in the global production networks. Applying difference-of-logs (growth rate) models, the results present robust positive effects of transactional networks on economic growth net of four groups of alternatively hypothesized determinants (dependence, industrialization, human capital investment, and military expenditure). The results indicate that increased transactional linkages with the international economy have been beneficial rather than harmful to economic growth. I conclude that structural position in external transaction networks has been the critical factor affecting growth and transformation in the world economy
A survey of statistical network models
Networks are ubiquitous in science and have become a focal point for
discussion in everyday life. Formal statistical models for the analysis of
network data have emerged as a major topic of interest in diverse areas of
study, and most of these involve a form of graphical representation.
Probability models on graphs date back to 1959. Along with empirical studies in
social psychology and sociology from the 1960s, these early works generated an
active network community and a substantial literature in the 1970s. This effort
moved into the statistical literature in the late 1970s and 1980s, and the past
decade has seen a burgeoning network literature in statistical physics and
computer science. The growth of the World Wide Web and the emergence of online
networking communities such as Facebook, MySpace, and LinkedIn, and a host of
more specialized professional network communities has intensified interest in
the study of networks and network data. Our goal in this review is to provide
the reader with an entry point to this burgeoning literature. We begin with an
overview of the historical development of statistical network modeling and then
we introduce a number of examples that have been studied in the network
literature. Our subsequent discussion focuses on a number of prominent static
and dynamic network models and their interconnections. We emphasize formal
model descriptions, and pay special attention to the interpretation of
parameters and their estimation. We end with a description of some open
problems and challenges for machine learning and statistics.Comment: 96 pages, 14 figures, 333 reference
EUSN 2021 Book of Abstracts, Fifth European Conference on Social Networks
Book of abstract of the fifth European conference on Social Networks EUSN 202
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