5,463 research outputs found
Identifying the underlying structure and dynamic interactions in a voting network
We analyse the structure and behaviour of a specific voting network using a
dynamic structure-based methodology which draws on Q-Analysis and social
network theory. Our empirical focus is on the Eurovision Song Contest over a
period of 20 years. For a multicultural contest of this kind, one of the key
questions is how the quality of a song is judged and how voting groups emerge.
We investigate structures that may identify the winner based purely on the
topology of the network. This provides a basic framework to identify what the
characteristics associated with becoming a winner are, and may help to
establish a homogenous criterion for subjective measures such as quality.
Further, we measure the importance of voting cliques, and present a dynamic
model based on a changing multidimensional measure of connectivity in order to
reveal the formation of emerging community structure within the contest.
Finally, we study the dynamic behaviour exhibited by the network in order to
understand the clustering of voting preferences and the relationship between
local and global properties.Comment: 20 pages, 10 figures, 3 tables, submitted to Physica
ĂlĆlĂ©nyek kollektĂv viselkedĂ©sĂ©nek statisztikus fizikĂĄja = Statistical physics of the collective behaviour of organisms
Experiments: We have carried out quantitative experiments on the collective motion of cells as a function of their density. A sharp transition could be observed from the random motility in sparse cultures to the flocking of dense islands of cells. Using ultra light GPS devices developed by us, we have determined the existing hierarchical relations within a flock of 10 homing pigeons. Modelling: From the simulations of our new model of flocking we concluded that the information exchange between particles was maximal at the critical point, in which the interplay of such factors as the level of noise, the tendency to follow the direction and the acceleration of others results in large fluctuations. Analysis: We have proposed a novel link-density based approach to finding overlapping communities in large networks. The algorithm used for the implementation of this technique is very efficient for most real networks, and provides full statistics quickly. Correspondingly, we have developed a by now popular, user-friendly, freely downloadable software for finding overlapping communities. Extending our method to the time-dependent regime, we found that large groups in evolving networks persist for longer if they are capable of dynamically altering their membership, thus, an ability to change the group composition results in better adaptability. We also showed that knowledge of the time commitment of members to a given community can be used for estimating the community's lifetime. Experiments: We have carried out quantitative experiments on the collective motion of cells as a function of their density. A sharp transition could be observed from the random motility in sparse cultures to the flocking of dense islands of cells. Using ultra light GPS devices developed by us, we have determined the existing hierarchical relations within a flock of 10 homing pigeons. Modelling: From the simulations of our new model of flocking we concluded that the information exchange between particles was maximal at the critical point, in which the interplay of such factors as the level of noise, the tendency to follow the direction and the acceleration of others results in large fluctuations. Analysis: We have proposed a novel link-density based approach to finding overlapping communities in large networks. The algorithm used for the implementation of this technique is very efficient for most real networks, and provides full statistics quickly. Correspondingly, we have developed a by now popular, user-friendly, freely downloadable software for finding overlapping communities. Extending our method to the time-dependent regime, we found that large groups in evolving networks persist for longer if they are capable of dynamically altering their membership, thus, an ability to change the group composition results in better adaptability. We also showed that knowledge of the time commitment of members to a given community can be used for estimating the community's lifetime
Characterizing the community structure of complex networks
Community structure is one of the key properties of complex networks and
plays a crucial role in their topology and function. While an impressive amount
of work has been done on the issue of community detection, very little
attention has been so far devoted to the investigation of communities in real
networks. We present a systematic empirical analysis of the statistical
properties of communities in large information, communication, technological,
biological, and social networks. We find that the mesoscopic organization of
networks of the same category is remarkably similar. This is reflected in
several characteristics of community structure, which can be used as
``fingerprints'' of specific network categories. While community size
distributions are always broad, certain categories of networks consist mainly
of tree-like communities, while others have denser modules. Average path
lengths within communities initially grow logarithmically with community size,
but the growth saturates or slows down for communities larger than a
characteristic size. This behaviour is related to the presence of hubs within
communities, whose roles differ across categories. Also the community
embeddedness of nodes, measured in terms of the fraction of links within their
communities, has a characteristic distribution for each category. Our findings
are verified by the use of two fundamentally different community detection
methods.Comment: 15 pages, 20 figures, 4 table
Overlapping modularity at the critical point of k-clique percolation
One of the most remarkable social phenomena is the formation of communities
in social networks corresponding to families, friendship circles, work teams,
etc. Since people usually belong to several different communities at the same
time, the induced overlaps result in an extremely complicated web of the
communities themselves. Thus, uncovering the intricate community structure of
social networks is a non-trivial task with great potential for practical
applications, gaining a notable interest in the recent years. The Clique
Percolation Method (CPM) is one of the earliest overlapping community finding
methods, which was already used in the analysis of several different social
networks. In this approach the communities correspond to k-clique percolation
clusters, and the general heuristic for setting the parameters of the method is
to tune the system just below the critical point of k-clique percolation.
However, this rule is based on simple physical principles and its validity was
never subject to quantitative analysis. Here we examine the quality of the
partitioning in the vicinity of the critical point using recently introduced
overlapping modularity measures. According to our results on real social- and
other networks, the overlapping modularities show a maximum close to the
critical point, justifying the original criteria for the optimal parameter
settings.Comment: 20 pages, 6 figure
Inferring Social Status and Rich Club Effects in Enterprise Communication Networks
Social status, defined as the relative rank or position that an individual
holds in a social hierarchy, is known to be among the most important motivating
forces in social behaviors. In this paper, we consider the notion of status
from the perspective of a position or title held by a person in an enterprise.
We study the intersection of social status and social networks in an
enterprise. We study whether enterprise communication logs can help reveal how
social interactions and individual status manifest themselves in social
networks. To that end, we use two enterprise datasets with three communication
channels --- voice call, short message, and email --- to demonstrate the
social-behavioral differences among individuals with different status. We have
several interesting findings and based on these findings we also develop a
model to predict social status. On the individual level, high-status
individuals are more likely to be spanned as structural holes by linking to
people in parts of the enterprise networks that are otherwise not well
connected to one another. On the community level, the principle of homophily,
social balance and clique theory generally indicate a "rich club" maintained by
high-status individuals, in the sense that this community is much more
connected, balanced and dense. Our model can predict social status of
individuals with 93% accuracy.Comment: 13 pages, 4 figure
Topics in social network analysis and network science
This chapter introduces statistical methods used in the analysis of social
networks and in the rapidly evolving parallel-field of network science.
Although several instances of social network analysis in health services
research have appeared recently, the majority involve only the most basic
methods and thus scratch the surface of what might be accomplished.
Cutting-edge methods using relevant examples and illustrations in health
services research are provided
Community structures and role detection in music networks
We analyze the existence of community structures in two different social
networks obtained from similarity and collaborative features between musical
artists. Our analysis reveals some characteristic organizational patterns and
provides information about the driving forces behind the growth of the
networks. In the similarity network, we find a strong correlation between
clusters of artists and musical genres. On the other hand, the collaboration
network shows two different kinds of communities: rather small structures
related to music bands and geographic zones, and much bigger communities built
upon collaborative clusters with a high number of participants related through
the period the artists were active. Finally, we detect the leading artists
inside their corresponding communities and analyze their roles in the network
by looking at a few topological properties of the nodes.Comment: 14 pages 7 figure
Detecting Community Structure in Dynamic Social Networks Using the Concept of Leadership
Detecting community structure in social networks is a fundamental problem
empowering us to identify groups of actors with similar interests. There have
been extensive works focusing on finding communities in static networks,
however, in reality, due to dynamic nature of social networks, they are
evolving continuously. Ignoring the dynamic aspect of social networks, neither
allows us to capture evolutionary behavior of the network nor to predict the
future status of individuals. Aside from being dynamic, another significant
characteristic of real-world social networks is the presence of leaders, i.e.
nodes with high degree centrality having a high attraction to absorb other
members and hence to form a local community. In this paper, we devised an
efficient method to incrementally detect communities in highly dynamic social
networks using the intuitive idea of importance and persistence of community
leaders over time. Our proposed method is able to find new communities based on
the previous structure of the network without recomputing them from scratch.
This unique feature, enables us to efficiently detect and track communities
over time rapidly. Experimental results on the synthetic and real-world social
networks demonstrate that our method is both effective and efficient in
discovering communities in dynamic social networks
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