Unsupervised Clustering Analysis: a Multiscale Complex Networks Approach

  title={Unsupervised Clustering Analysis: a Multiscale Complex Networks Approach},
  author={Clara Granell and Sergio G{\'o}mez and Alex Arenas},
  journal={Int. J. Bifurc. Chaos},
Unsupervised clustering, also known as natural clustering, stands for the classification of data according to their similarities. Here we study this problem from the perspective of complex networks. Mapping the description of data similarities to graphs, we propose to extend two multiresolution modularity based algorithms to the finding of modules (clusters) in general data sets producing a multiscales' solution. We show the performance of these reported algorithms to the classification of a… 

Figures from this paper

An Evolutionary Multiobjective Approach for Community Discovery in Dynamic Networks

The detection of communities with temporal smoothness is formulated as a multiobjective problem and a method based on genetic algorithms is proposed and the main advantage of the algorithm is that it automatically provides a solution representing the best trade-off between the accuracy of the clustering obtained, and the deviation from one time step to the successive.

Hierarchical Multiresolution Method to Overcome the Resolution Limit in Complex Networks

A new hierarchical multiresolution scheme that works even when the network decomposition is very close to the resolution limit is presented, and a new algorithm is proposed to speed up the computational cost of screening the mesoscale looking for the resolution parameter that best splits every subgraph.

Community structure in temporal multilayer networks, and its application to financial correlation networks

This thesis proposes a benchmark for community detection in temporal networks and carries out various numerical experiments to compare the performance of different methods and computational heuristics on the authors' benchmark.

Graph clustering with local search optimization: the resolution bias of the objective function matters most.

It is shown experimentally that LSO often achieves superior performance than spectral clustering on various benchmark, real-life, and k-nearest-neighbor graphs, and the flexibility of LSO and its efficiency, provide arguments in favor of this optimization method.

Mining and Analyzing the Italian Parliament: Party Structure and Evolution

The analysis performed revealed as a valuable tool in detecting trends and drifts of Parliamentarians and showed its effectiveness at identifying political parties and at providing insights on the temporal evolution of groups and their cohesiveness without having at disposal any knowledge about political membership of Representatives.

Analyzing Voting Behavior in Italian Parliament: Group Cohesion and Evolution

  • Alessia AmelioC. Pizzuti
  • Political Science
    2012 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining
  • 2012
The roll calls of the Italian Parliament in the current legislature is studied by employing multidimensional scaling, hierarchical clustering, and network analysis, which showed its effectiveness at identifying political parties and at providing insights on the temporal evolution of groups and their cohesiveness.

Using network theory to identify disease outbreaks of unknown etiology

The identification of undiagnosed disease outbreaks is critical for mobilizing efforts to prevent widespread transmission of novel virulent pathogens. Recent developments in online surveillance

Radboud Repository of the Radboud University

Background: Chronic fatigue syndrome (CFS) is characterized by profound and disabling fatigue with no known somatic explanation. Cognitive behavioral therapy (CBT) has proven to be a successful



Data clustering: a review

An overview of pattern clustering methods from a statistical pattern recognition perspective is presented, with a goal of providing useful advice and references to fundamental concepts accessible to the broad community of clustering practitioners.

Clustering algorithm for determining community structure in large networks.

The results suggest that the proposed algorithm is a good choice to analyze the community structure of medium and large networks in the range of tens and hundreds of thousand vertices.

Analysis of community structure in networks of correlated data.

A reformulation of modularity that allows the analysis of the community structure in networks of correlated data and preserves the probabilistic semantics of the original definition even when the network is directed, weighted, signed, and has self-loops.

Natural clustering: the modularity approach

We show that modularity, a quantity introduced in the study of networked systems, can be generalized and used in the clustering problem as an indicator for the quality of the solution. The

Community detection in graphs

Survey of clustering algorithms

  • R. XuD. Wunsch
  • Computer Science
    IEEE Transactions on Neural Networks
  • 2005
Clustering algorithms for data sets appearing in statistics, computer science, and machine learning are surveyed, and their applications in some benchmark data sets, the traveling salesman problem, and bioinformatics, a new field attracting intensive efforts are illustrated.

Detecting fuzzy community structures in complex networks with a Potts model.

A fast community detection algorithm based on a q-state Potts model that allows for the detection of overlapping ("fuzzy") communities and quantifying the association of nodes with multiple communities as well as the robustness of a community.

Cartography of complex networks: modules and universal roles

It is demonstrated that one can (i) find modules in complex networks and (ii) classify nodes into universal roles according to their pattern of within- and between-module connections, which yields a ‘cartographic representation’ of complex networks.

Fast algorithm for detecting community structure in networks.

  • M. Newman
  • Computer Science
    Physical review. E, Statistical, nonlinear, and soft matter physics
  • 2004
An algorithm is described which gives excellent results when tested on both computer-generated and real-world networks and is much faster, typically thousands of times faster, than previous algorithms.