42,817 research outputs found
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
Structure and Dynamics of Information Pathways in Online Media
Diffusion of information, spread of rumors and infectious diseases are all
instances of stochastic processes that occur over the edges of an underlying
network. Many times networks over which contagions spread are unobserved, and
such networks are often dynamic and change over time. In this paper, we
investigate the problem of inferring dynamic networks based on information
diffusion data. We assume there is an unobserved dynamic network that changes
over time, while we observe the results of a dynamic process spreading over the
edges of the network. The task then is to infer the edges and the dynamics of
the underlying network.
We develop an on-line algorithm that relies on stochastic convex optimization
to efficiently solve the dynamic network inference problem. We apply our
algorithm to information diffusion among 3.3 million mainstream media and blog
sites and experiment with more than 179 million different pieces of information
spreading over the network in a one year period. We study the evolution of
information pathways in the online media space and find interesting insights.
Information pathways for general recurrent topics are more stable across time
than for on-going news events. Clusters of news media sites and blogs often
emerge and vanish in matter of days for on-going news events. Major social
movements and events involving civil population, such as the Libyan's civil war
or Syria's uprise, lead to an increased amount of information pathways among
blogs as well as in the overall increase in the network centrality of blogs and
social media sites.Comment: To Appear at the 6th International Conference on Web Search and Data
Mining (WSDM '13
Foundations and modelling of dynamic networks using Dynamic Graph Neural Networks: A survey
Dynamic networks are used in a wide range of fields, including social network
analysis, recommender systems, and epidemiology. Representing complex networks
as structures changing over time allow network models to leverage not only
structural but also temporal patterns. However, as dynamic network literature
stems from diverse fields and makes use of inconsistent terminology, it is
challenging to navigate. Meanwhile, graph neural networks (GNNs) have gained a
lot of attention in recent years for their ability to perform well on a range
of network science tasks, such as link prediction and node classification.
Despite the popularity of graph neural networks and the proven benefits of
dynamic network models, there has been little focus on graph neural networks
for dynamic networks. To address the challenges resulting from the fact that
this research crosses diverse fields as well as to survey dynamic graph neural
networks, this work is split into two main parts. First, to address the
ambiguity of the dynamic network terminology we establish a foundation of
dynamic networks with consistent, detailed terminology and notation. Second, we
present a comprehensive survey of dynamic graph neural network models using the
proposed terminologyComment: 28 pages, 9 figures, 8 table
Asynchronous Networks and Event Driven Dynamics
Real-world networks in technology, engineering and biology often exhibit
dynamics that cannot be adequately reproduced using network models given by
smooth dynamical systems and a fixed network topology. Asynchronous networks
give a theoretical and conceptual framework for the study of network dynamics
where nodes can evolve independently of one another, be constrained, stop, and
later restart, and where the interaction between different components of the
network may depend on time, state, and stochastic effects. This framework is
sufficiently general to encompass a wide range of applications ranging from
engineering to neuroscience. Typically, dynamics is piecewise smooth and there
are relationships with Filippov systems. In the first part of the paper, we
give examples of asynchronous networks, and describe the basic formalism and
structure. In the second part, we make the notion of a functional asynchronous
network rigorous, discuss the phenomenon of dynamical locks, and present a
foundational result on the spatiotemporal factorization of the dynamics for a
large class of functional asynchronous networks
Dynamical Systems to Monitor Complex Networks in Continuous Time
In many settings it is appropriate to treat the evolution of pairwise
interactions over continuous time. We show that new Katz-style centrality
measures can be derived in this context via solutions to a nonautonomous ODE
driven by the network dynamics. This allows us to identify and track, at any
resolution, the most influential nodes in terms of broadcasting and receiving
information through time dependent links. In addition to the classical notion
of attenuation across edges used in the static Katz centrality measure, the ODE
also allows for attenuation over time, so that real time "running measures" can
be computed. With regard to computational efficiency, we explain why it is
cheaper to track good receivers of information than good broadcasters. We
illustrate the new measures on a large scale voice call network, where key
features are discovered that are not evident from snapshots or aggregates
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