Rocco Langone | KU Leuven (original) (raw)
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Papers by Rocco Langone
Understanding Complex Systems, 2015
IEEE Transactions on Neural Networks and Learning Systems, 2015
Soft clustering algorithms can handle real-life datasets better as they capture the presence of i... more Soft clustering algorithms can handle real-life datasets better as they capture the presence of inherent overlapping clusters. A soft kernel spectral clustering (SKSC) method proposed in [1] exploited the eigen-projections of the points to assign them different cluster membership probabilities. In this paper, we detect points in dense overlapping regions as overlap points. We also identify the outlier points by exploiting the eigen-projections. We then propose novel ranking techniques using structure and similarity properties in the eigen-space to rank these overlap and outlier points. By ranking the overlap and outlier points we provide an order for the most and least influential points in the dataset. We demonstrate the effectiveness of our ranking measures on several datasets.
PLOS ONE, 2015
Real-world complex networks are dynamic in nature and change over time. The change is usually obs... more Real-world complex networks are dynamic in nature and change over time. The change is usually observed in the interactions within the network over time. Complex networks exhibit community like structures. A key feature of the dynamics of complex networks is the evolution of communities over time. Several methods have been proposed to detect and track the evolution of these groups over time. However, there is no generic tool which visualizes all the aspects of group evolution in dynamic networks including birth, death, splitting, merging, expansion, shrinkage and continuation of groups. In this paper, we propose Netgram: a tool for visualizing evolution of communities in time-evolving graphs. Netgram maintains evolution of communities over 2 consecutive time-stamps in tables which are used to create a query database using the sql outer-join operation. It uses a line-based visualization technique which adheres to certain design principles and aesthetic guidelines. Netgram uses a greedy solution to order the initial community information provided by the evolutionary clustering technique such that we have fewer line cross-overs in the visualization. This makes it easier to track the progress of individual communities in time evolving graphs. Netgram is a generic toolkit which can be used with any evolutionary community detection algorithm as illustrated in our experiments. We use Netgram for visualization of topic evolution in the NIPS conference over a period of 11 years and observe the emergence and merging of several disciplines in the field of information processing systems.
Kernel Spectral Clustering (KSC) solves a weighted kernel principal component analysis problem in... more Kernel Spectral Clustering (KSC) solves a weighted kernel principal component analysis problem in a primal-dual optimization framework. It results in a clustering model using the dual solution of the problem. It has a powerful out-of-sample extension property leading to good clustering generalization w.r.t. the unseen data points. The out-of-sample extension property allows to build a sparse model on a small training set and introduces the first level of sparsity. The clustering dual model is expressed in terms of non-sparse kernel expansions where every point in the training set contributes. The goal is to find reduced set of training points which can best approximate the original solution. In this paper a second level of sparsity is introduced in order to reduce the time complexity of the computationally expensive out-of-sample extension. In this paper we investigate various penalty based reduced set techniques including the Group Lasso, L0, L1+L0 penalizations and compare the amo...
2015 IEEE International Conference on Big Data (Big Data), 2015
2014 Ieee Symposium on Computational Intelligence in Multi Criteria Decision Making, Dec 1, 2014
Asia Pacific Journal of Atmospheric Sciences, Aug 1, 2009
PLOS ONE, 2015
Real-world complex networks are dynamic in nature and change over time. The change is usually obs... more Real-world complex networks are dynamic in nature and change over time. The change is usually observed in the interactions within the network over time. Complex networks exhibit community like structures. A key feature of the dynamics of complex networks is the evolution of communities over time. Several methods have been proposed to detect and track the evolution of these groups over time. However, there is no generic tool which visualizes all the aspects of group evolution in dynamic networks including birth, death, splitting, merging, expansion, shrinkage and continuation of groups. In this paper, we propose Netgram: a tool for visualizing evolution of communities in time-evolving graphs. Netgram maintains evolution of communities over 2 consecutive time-stamps in tables which are used to create a query database using the sql outer-join operation. It uses a line-based visualization technique which adheres to certain design principles and aesthetic guidelines. Netgram uses a greedy solution to order the initial community information provided by the evolutionary clustering technique such that we have fewer line cross-overs in the visualization. This makes it easier to track the progress of individual communities in time evolving graphs. Netgram is a generic toolkit which can be used with any evolutionary community detection algorithm as illustrated in our experiments. We use Netgram for visualization of topic evolution in the NIPS conference over a period of 11 years and observe the emergence and merging of several disciplines in the field of information processing systems.
Understanding Complex Systems, 2015
2014 IEEE Symposium on Computational Intelligence in Multi-Criteria Decision-Making (MCDM), 2014
Understanding Complex Systems, 2015
IEEE Transactions on Neural Networks and Learning Systems, 2015
Soft clustering algorithms can handle real-life datasets better as they capture the presence of i... more Soft clustering algorithms can handle real-life datasets better as they capture the presence of inherent overlapping clusters. A soft kernel spectral clustering (SKSC) method proposed in [1] exploited the eigen-projections of the points to assign them different cluster membership probabilities. In this paper, we detect points in dense overlapping regions as overlap points. We also identify the outlier points by exploiting the eigen-projections. We then propose novel ranking techniques using structure and similarity properties in the eigen-space to rank these overlap and outlier points. By ranking the overlap and outlier points we provide an order for the most and least influential points in the dataset. We demonstrate the effectiveness of our ranking measures on several datasets.
PLOS ONE, 2015
Real-world complex networks are dynamic in nature and change over time. The change is usually obs... more Real-world complex networks are dynamic in nature and change over time. The change is usually observed in the interactions within the network over time. Complex networks exhibit community like structures. A key feature of the dynamics of complex networks is the evolution of communities over time. Several methods have been proposed to detect and track the evolution of these groups over time. However, there is no generic tool which visualizes all the aspects of group evolution in dynamic networks including birth, death, splitting, merging, expansion, shrinkage and continuation of groups. In this paper, we propose Netgram: a tool for visualizing evolution of communities in time-evolving graphs. Netgram maintains evolution of communities over 2 consecutive time-stamps in tables which are used to create a query database using the sql outer-join operation. It uses a line-based visualization technique which adheres to certain design principles and aesthetic guidelines. Netgram uses a greedy solution to order the initial community information provided by the evolutionary clustering technique such that we have fewer line cross-overs in the visualization. This makes it easier to track the progress of individual communities in time evolving graphs. Netgram is a generic toolkit which can be used with any evolutionary community detection algorithm as illustrated in our experiments. We use Netgram for visualization of topic evolution in the NIPS conference over a period of 11 years and observe the emergence and merging of several disciplines in the field of information processing systems.
Kernel Spectral Clustering (KSC) solves a weighted kernel principal component analysis problem in... more Kernel Spectral Clustering (KSC) solves a weighted kernel principal component analysis problem in a primal-dual optimization framework. It results in a clustering model using the dual solution of the problem. It has a powerful out-of-sample extension property leading to good clustering generalization w.r.t. the unseen data points. The out-of-sample extension property allows to build a sparse model on a small training set and introduces the first level of sparsity. The clustering dual model is expressed in terms of non-sparse kernel expansions where every point in the training set contributes. The goal is to find reduced set of training points which can best approximate the original solution. In this paper a second level of sparsity is introduced in order to reduce the time complexity of the computationally expensive out-of-sample extension. In this paper we investigate various penalty based reduced set techniques including the Group Lasso, L0, L1+L0 penalizations and compare the amo...
2015 IEEE International Conference on Big Data (Big Data), 2015
2014 Ieee Symposium on Computational Intelligence in Multi Criteria Decision Making, Dec 1, 2014
Asia Pacific Journal of Atmospheric Sciences, Aug 1, 2009
PLOS ONE, 2015
Real-world complex networks are dynamic in nature and change over time. The change is usually obs... more Real-world complex networks are dynamic in nature and change over time. The change is usually observed in the interactions within the network over time. Complex networks exhibit community like structures. A key feature of the dynamics of complex networks is the evolution of communities over time. Several methods have been proposed to detect and track the evolution of these groups over time. However, there is no generic tool which visualizes all the aspects of group evolution in dynamic networks including birth, death, splitting, merging, expansion, shrinkage and continuation of groups. In this paper, we propose Netgram: a tool for visualizing evolution of communities in time-evolving graphs. Netgram maintains evolution of communities over 2 consecutive time-stamps in tables which are used to create a query database using the sql outer-join operation. It uses a line-based visualization technique which adheres to certain design principles and aesthetic guidelines. Netgram uses a greedy solution to order the initial community information provided by the evolutionary clustering technique such that we have fewer line cross-overs in the visualization. This makes it easier to track the progress of individual communities in time evolving graphs. Netgram is a generic toolkit which can be used with any evolutionary community detection algorithm as illustrated in our experiments. We use Netgram for visualization of topic evolution in the NIPS conference over a period of 11 years and observe the emergence and merging of several disciplines in the field of information processing systems.
Understanding Complex Systems, 2015
2014 IEEE Symposium on Computational Intelligence in Multi-Criteria Decision-Making (MCDM), 2014