408,355 research outputs found
Information dynamics algorithm for detecting communities in networks
The problem of community detection is relevant in many scientific
disciplines, from social science to statistical physics. Given the impact of
community detection in many areas, such as psychology and social sciences, we
have addressed the issue of modifying existing well performing algorithms by
incorporating elements of the domain application fields, i.e. domain-inspired.
We have focused on a psychology and social network - inspired approach which
may be useful for further strengthening the link between social network studies
and mathematics of community detection. Here we introduce a community-detection
algorithm derived from the van Dongen's Markov Cluster algorithm (MCL) method
by considering networks' nodes as agents capable to take decisions. In this
framework we have introduced a memory factor to mimic a typical human behavior
such as the oblivion effect. The method is based on information diffusion and
it includes a non-linear processing phase. We test our method on two classical
community benchmark and on computer generated networks with known community
structure. Our approach has three important features: the capacity of detecting
overlapping communities, the capability of identifying communities from an
individual point of view and the fine tuning the community detectability with
respect to prior knowledge of the data. Finally we discuss how to use a Shannon
entropy measure for parameter estimation in complex networks.Comment: Submitted to "Communication in Nonlinear Science and Numerical
Simulation
The Role of Prior Knowledge in Multi-Population Cultural Algorithms for Community Detection in Dynamic Social Networks
The relationship between a community and the knowledge that it encompasses is a fundamentally important aspect of any social network. Communities, with some level of similarity, implicitly tend to have some level of similarity in their knowledge as well. This work does the analysis on the role of prior knowledge in Multi-Population Cultural Algorithm (MPCA) for community detection in dynamic social networks. MPCA can be used to find the communities in a social network. The knowledge gained in this process is useful to analyze the communities in other social networks having some level of similarity. Our work assumes that knowledge is an integral part of any community of a social network and plays a very important role in its evolution. Different types of networks with levels of non-similarity are analyzed to see the role of prior knowledge while finding communities in them
Place Categorization and Semantic Mapping on a Mobile Robot
In this paper we focus on the challenging problem of place categorization and
semantic mapping on a robot without environment-specific training. Motivated by
their ongoing success in various visual recognition tasks, we build our system
upon a state-of-the-art convolutional network. We overcome its closed-set
limitations by complementing the network with a series of one-vs-all
classifiers that can learn to recognize new semantic classes online. Prior
domain knowledge is incorporated by embedding the classification system into a
Bayesian filter framework that also ensures temporal coherence. We evaluate the
classification accuracy of the system on a robot that maps a variety of places
on our campus in real-time. We show how semantic information can boost robotic
object detection performance and how the semantic map can be used to modulate
the robot's behaviour during navigation tasks. The system is made available to
the community as a ROS module
A New overlapping community detection algorithm based on similarity of neighbors in complex networks
summary:Community detection algorithms help us improve the management of complex networks and provide a clean sight of them. We can encounter complex networks in various fields such as social media, bioinformatics, recommendation systems, and search engines. As the definition of the community changes based on the problem considered, there is no algorithm that works universally for all kinds of data and network structures. Communities can be disjointed such that each member is in at most one community or overlapping such that every member is in at least one community. In this study, we examine the problem of finding overlapping communities in complex networks and propose a new algorithm based on the similarity of neighbors. This algorithm runs in running time in the complex network containing number of relationships. To compare our algorithm with existing ones, we select the most successful four algorithms from the Community Detection library (CDlib) by eliminating the algorithms that require prior knowledge, are unstable, and are time-consuming. We evaluate the successes of the proposed algorithm and the selected algorithms using various known metrics such as modularity, F-score, and Normalized Mutual Information. In addition, we adapt the coverage metric defined for disjoint communities to overlapping communities and also make comparisons with this metric. We also test all of the algorithms on small graphs of real communities. The experimental results show that the proposed algorithm is successful in finding overlapping communities
- …