7 research outputs found
Identifying communities by influence dynamics in social networks
Communities are not static; they evolve, split and merge, appear and
disappear, i.e. they are product of dynamical processes that govern the
evolution of the network. A good algorithm for community detection should not
only quantify the topology of the network, but incorporate the dynamical
processes that take place on the network. We present a novel algorithm for
community detection that combines network structure with processes that support
creation and/or evolution of communities. The algorithm does not embrace the
universal approach but instead tries to focus on social networks and model
dynamic social interactions that occur on those networks. It identifies
leaders, and communities that form around those leaders. It naturally supports
overlapping communities by associating each node with a membership vector that
describes node's involvement in each community. This way, in addition to
overlapping communities, we can identify nodes that are good followers to their
leader, and also nodes with no clear community involvement that serve as a
proxy between several communities and are equally as important. We run the
algorithm for several real social networks which we believe represent a good
fraction of the wide body of social networks and discuss the results including
other possible applications.Comment: 10 pages, 6 figure
Structure controllability of complex network based on preferential matching
Minimum driver node sets (MDSs) play an important role in studying the
structural controllability of complex networks. Recent research has shown that
MDSs tend to avoid high-degree nodes. However, this observation is based on the
analysis of a small number of MDSs, because enumerating all of the MDSs of a
network is a #P problem. Therefore, past research has not been sufficient to
arrive at a convincing conclusion. In this paper, first, we propose a
preferential matching algorithm to find MDSs that have a specific degree
property. Then, we show that the MDSs obtained by preferential matching can be
composed of high- and medium-degree nodes. Moreover, the experimental results
also show that the average degree of the MDSs of some networks tends to be
greater than that of the overall network, even when the MDSs are obtained using
previous research method. Further analysis shows that whether the driver nodes
tend to be high-degree nodes or not is closely related to the edge direction of
the network
Network communities of dynamical influence
Fuelled by a desire for greater connectivity, networked systems now pervade our society at an unprecedented level that will affect it in ways we do not yet understand. In contrast, nature has already developed efficient networks that can instigate rapid response and consensus when key elements are stimulated. We present a technique for identifying these key elements by investigating the relationships between a system’s most dominant eigenvectors. This approach reveals the most effective vertices for leading a network to rapid consensus when stimulated, as well as the communities that form under their dynamical influence. In applying this technique, the effectiveness of starling flocks was found to be due, in part, to the low outdegree of every bird, where increasing the number of outgoing connections can produce a less responsive flock. A larger outdegree also affects the location of the birds with the most influence, where these influentially connected birds become more centrally located and in a poorer position to observe a predator and, hence, instigate an evasion manoeuvre. Finally, the technique was found to be effective in large voxel-wise brain connectomes where subjects can be identified from their influential communities
The Odyssey’s mythological network
We are grateful to Maurício A. Ribeiro which fitted the power law with cut-off degree distribution for Facebook’s and Odyssey’s networks. This work was supported by 1) Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES) to PJM, and 2) Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq) to SESP.Peer reviewedPublisher PD
Detección y análisis de comunidades en redes sociales (#TodosSomosPolitécnico)
Este artículo plantea la utilización del método de mapas jerárquicos para la detección de comunidades en una red social. El corpus utilizado en este artículo está basado en la interacción realizada en Twitter por un conjunto de actores, en el movimiento estudiantil del 2014 #TodosSomosPolitécnico. Al aplicar los mapas jerárquicos es posible identificar un conjunto de comunidades muy bien definidas en torno al movimiento. Un análisis basado en la visualización de las comunidades es realizado para corroborar la pertinencia de la detección.Palabra(s) Clave(s): comunidades, mapas jerárquicos, redes sociales, Twitter
Local dominance unveils clusters in networks
Clusters or communities can provide a coarse-grained description of complex
systems at multiple scales, but their detection remains challenging in
practice. Community detection methods often define communities as dense
subgraphs, or subgraphs with few connections in-between, via concepts such as
the cut, conductance, or modularity. Here we consider another perspective built
on the notion of local dominance, where low-degree nodes are assigned to the
basin of influence of high-degree nodes, and design an efficient algorithm
based on local information. Local dominance gives rises to community centers,
and uncovers local hierarchies in the network. Community centers have a larger
degree than their neighbors and are sufficiently distant from other centers.
The strength of our framework is demonstrated on synthesized and empirical
networks with ground-truth community labels. The notion of local dominance and
the associated asymmetric relations between nodes are not restricted to
community detection, and can be utilised in clustering problems, as we
illustrate on networks derived from vector data