27 research outputs found
Robust network community detection using balanced propagation
Label propagation has proven to be an extremely fast method for detecting
communities in large complex networks. Furthermore, due to its simplicity, it
is also currently one of the most commonly adopted algorithms in the
literature. Despite various subsequent advances, an important issue of the
algorithm has not yet been properly addressed. Random (node) update orders
within the algorithm severely hamper its robustness, and consequently also the
stability of the identified community structure. We note that an update order
can be seen as increasing propagation preferences from certain nodes, and
propose a balanced propagation that counteracts for the introduced randomness
by utilizing node balancers. We have evaluated the proposed approach on
synthetic networks with planted partition, and on several real-world networks
with community structure. The results confirm that balanced propagation is
significantly more robust than label propagation, when the performance of
community detection is even improved. Thus, balanced propagation retains high
scalability and algorithmic simplicity of label propagation, but improves on
its stability and performance
Software systems through complex networks science: Review, analysis and applications
Complex software systems are among most sophisticated human-made systems, yet
only little is known about the actual structure of 'good' software. We here
study different software systems developed in Java from the perspective of
network science. The study reveals that network theory can provide a prominent
set of techniques for the exploratory analysis of large complex software
system. We further identify several applications in software engineering, and
propose different network-based quality indicators that address software
design, efficiency, reusability, vulnerability, controllability and other. We
also highlight various interesting findings, e.g., software systems are highly
vulnerable to processes like bug propagation, however, they are not easily
controllable
Forman-Ricci flow for change detection in large dynamic data sets
We present a viable solution to the challenging question of change detection
in complex networks inferred from large dynamic data sets. Building on Forman's
discretization of the classical notion of Ricci curvature, we introduce a novel
geometric method to characterize different types of real-world networks with an
emphasis on peer-to-peer networks. Furthermore we adapt the classical Ricci
flow that already proved to be a powerful tool in image processing and
graphics, to the case of undirected and weighted networks. The application of
the proposed method on peer-to-peer networks yields insights into topological
properties and the structure of their underlying data.Comment: Conference paper, accepted at ICICS 2016. (Updated version
Computing Vertex Centrality Measures in Massive Real Networks with a Neural Learning Model
Vertex centrality measures are a multi-purpose analysis tool, commonly used
in many application environments to retrieve information and unveil knowledge
from the graphs and network structural properties. However, the algorithms of
such metrics are expensive in terms of computational resources when running
real-time applications or massive real world networks. Thus, approximation
techniques have been developed and used to compute the measures in such
scenarios. In this paper, we demonstrate and analyze the use of neural network
learning algorithms to tackle such task and compare their performance in terms
of solution quality and computation time with other techniques from the
literature. Our work offers several contributions. We highlight both the pros
and cons of approximating centralities though neural learning. By empirical
means and statistics, we then show that the regression model generated with a
feedforward neural networks trained by the Levenberg-Marquardt algorithm is not
only the best option considering computational resources, but also achieves the
best solution quality for relevant applications and large-scale networks.
Keywords: Vertex Centrality Measures, Neural Networks, Complex Network Models,
Machine Learning, Regression ModelComment: 8 pages, 5 tables, 2 figures, version accepted at IJCNN 2018. arXiv
admin note: text overlap with arXiv:1810.1176
Community detection in complex networks using flow simulation
Community detection and analysis is an important part of studying the organization of complex systems in real world, and it�s extensively applied on many fields. Recently, many of existing algorithms are not effective or the results are unstable. In this paper, a new method of community testing is proposed by us based on the conception of flow field. In our approach, each node is represented as a field source and has a tendency to forward data to the connected nodes with highest field strength, after some iterations the nodes with same data information form a community. It is evaluated by us for the approach on some synthetic and real-world networks whose community structures are known. It is demonstrated that the approach performs wellin effectiveness and robustness. © 2017 Association for Computing Machinery