The Experts below are selected from a list of 246 Experts worldwide ranked by ideXlab platform
Thomas Seidl - One of the best experts on this subject based on the ideXlab platform.
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ICDM - Detecting Change Processes in Dynamic Networks by Frequent Graph Evolution Rule Mining
2016 IEEE 16th International Conference on Data Mining (ICDM), 2016Co-Authors: Erik Scharwachter, Emmanuel Muller, Jonathan F Donges, Marwan Hassani, Thomas SeidlAbstract:The analysis of the temporal Evolution of dynamic networks is a key challenge for understanding complex processes hidden in graph structured data. Graph Evolution Rules capture such processes on the level of small subgraphs by describing frequently occurring structural changes within a network. Existing Rule discovery methods make restrictive assumptions on the change processes present in networks. We propose EvoMine, a frequent graph Evolution Rule mining method that, for the first time, supports networks with edge insertions and deletions as well as node and edge relabelings. EvoMine defines embedding-based and event-based support as two novel measures to assess the frequency of Rules. These measures are based on novel mappings from dynamic networks to databases of union graphs that retain all Evolution information relevant for Rule mining. Using these mappings the Rule mining problem can be solved by frequent subgraph mining. We evaluate our approach and two baseline algorithms on several real datasets. To the best of our knowledge, this is the first empirical comparison of Rule mining algorithmsfor dynamic networks.
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detecting change processes in dynamic networks by frequent graph Evolution Rule mining
International Conference on Data Mining, 2016Co-Authors: Erik Scharwachter, Emmanuel Muller, Jonathan F Donges, Marwan Hassani, Thomas SeidlAbstract:The analysis of the temporal Evolution of dynamic networks is a key challenge for understanding complex processes hidden in graph structured data. Graph Evolution Rules capture such processes on the level of small subgraphs by describing frequently occurring structural changes within a network. Existing Rule discovery methods make restrictive assumptions on the change processes present in networks. We propose EvoMine, a frequent graph Evolution Rule mining method that, for the first time, supports networks with edge insertions and deletions as well as node and edge relabelings. EvoMine defines embedding-based and event-based support as two novel measures to assess the frequency of Rules. These measures are based on novel mappings from dynamic networks to databases of union graphs that retain all Evolution information relevant for Rule mining. Using these mappings the Rule mining problem can be solved by frequent subgraph mining. We evaluate our approach and two baseline algorithms on several real datasets. To the best of our knowledge, this is the first empirical comparison of Rule mining algorithmsfor dynamic networks.
Erik Scharwachter - One of the best experts on this subject based on the ideXlab platform.
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ICDM - Detecting Change Processes in Dynamic Networks by Frequent Graph Evolution Rule Mining
2016 IEEE 16th International Conference on Data Mining (ICDM), 2016Co-Authors: Erik Scharwachter, Emmanuel Muller, Jonathan F Donges, Marwan Hassani, Thomas SeidlAbstract:The analysis of the temporal Evolution of dynamic networks is a key challenge for understanding complex processes hidden in graph structured data. Graph Evolution Rules capture such processes on the level of small subgraphs by describing frequently occurring structural changes within a network. Existing Rule discovery methods make restrictive assumptions on the change processes present in networks. We propose EvoMine, a frequent graph Evolution Rule mining method that, for the first time, supports networks with edge insertions and deletions as well as node and edge relabelings. EvoMine defines embedding-based and event-based support as two novel measures to assess the frequency of Rules. These measures are based on novel mappings from dynamic networks to databases of union graphs that retain all Evolution information relevant for Rule mining. Using these mappings the Rule mining problem can be solved by frequent subgraph mining. We evaluate our approach and two baseline algorithms on several real datasets. To the best of our knowledge, this is the first empirical comparison of Rule mining algorithmsfor dynamic networks.
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detecting change processes in dynamic networks by frequent graph Evolution Rule mining
International Conference on Data Mining, 2016Co-Authors: Erik Scharwachter, Emmanuel Muller, Jonathan F Donges, Marwan Hassani, Thomas SeidlAbstract:The analysis of the temporal Evolution of dynamic networks is a key challenge for understanding complex processes hidden in graph structured data. Graph Evolution Rules capture such processes on the level of small subgraphs by describing frequently occurring structural changes within a network. Existing Rule discovery methods make restrictive assumptions on the change processes present in networks. We propose EvoMine, a frequent graph Evolution Rule mining method that, for the first time, supports networks with edge insertions and deletions as well as node and edge relabelings. EvoMine defines embedding-based and event-based support as two novel measures to assess the frequency of Rules. These measures are based on novel mappings from dynamic networks to databases of union graphs that retain all Evolution information relevant for Rule mining. Using these mappings the Rule mining problem can be solved by frequent subgraph mining. We evaluate our approach and two baseline algorithms on several real datasets. To the best of our knowledge, this is the first empirical comparison of Rule mining algorithmsfor dynamic networks.
Jorge Tejedor - One of the best experts on this subject based on the ideXlab platform.
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Workshop on Membrane Computing - Optimizing Evolution Rules application and communication times in membrane systems implementation
Membrane Computing, 2007Co-Authors: Jorge Tejedor, Luis Sánchez Fernández, Fernando Arroyo, Abraham Gutiérrez, Ginés Bravo, Sandra GómezAbstract:Several published time analyses in P systems implementation have proved that there is a very strong relationship between communication and Evolution Rules application time in membranes of the system. This work shows how to optimize the Evolution Rule application and communication times using two complementary techniques: the improvement of Evolution Rules algorithms and the usage of compression schema. On the one hand, this work uses the concepts of competitiveness relationship among active Rules and competitiveness graph. For this, it takes into account the fact that some active Rules in a membrane can consume disjoint object sets. Based on these concepts, we present a new Evolution Rules application algorithm that improves throughput of active Rules elimination algorithms (sequential and parallel). On the other hand, this work presents an algorithm for compressing information related to multisets and Evolution Rules, based on the assumption that algorithmic complexity of the operations performed over multisets, in Evolution Rules application algorithms, is determined by the representation of multiset information of these Rules. This representation also affects the communication phase among membranes phase.
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Algorithm of active Rules elimination for application of Evolution Rules
2007Co-Authors: Jorge Tejedor, Luis Sánchez Fernández, Fernando Arroyo, Abraham GutiérrezAbstract:This paper presents a new Evolution Rules application algorithm to a multiset of objects to use in the P system implementation in digital devices. In each step of this algorithm two main actions are carried out eliminating, at least, an Evolution Rule to the set of active Rules. Therefore, the number of operations executed is limited and it can be known a priori which is its execution time at worst. This is very important as it allows for determination of the number of membranes to be located in each processor in the distributed implementation architectures of P systems to obtain optimal times with minimal resources. Although the algorithm is sequential, it reaches a certain degree of parallelism due to a Rule that can be applied several times in a single step. In addition to this, this algorithm has shown in the experimental tests that the execution times is better than the ones previously published.
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BIOCOMP - New Algorithms for Application of Evolution Rules based on Applicability Benchmarks.
2006Co-Authors: Luis Sánchez Fernández, Fernando Arroyo, Juan Castellanos, Jorge Tejedor, Ivan GarciaAbstract:Transition P System are a parallel and distributed computational model based on the notion of the cellular membrane structure. Each membrane determines a region that encloses a multiset of objects and Evolution Rules. Transition P Systems evolve through transitions between two consecutives configurations. Moreover, transitions between two consecutive configurations are provided by an exhaustive non-deterministic and parallel application of Evolution Rules inside each membrane of the P system. Hence, Rules application is critical for the whole Evolution process efficiency, because it is performed in parallel inside each membrane in each one of the Evolution steps. The work presented here includes definitions of maximal and minimum applicability benchmarks of an Evolution Rule over a determined multiset of objects. These two definitions permit the design of new algorithms that improve complexity of traditional step by step one. This is achieved through new parallelism degree for the application of Evolution Rules.
Jonathan F Donges - One of the best experts on this subject based on the ideXlab platform.
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ICDM - Detecting Change Processes in Dynamic Networks by Frequent Graph Evolution Rule Mining
2016 IEEE 16th International Conference on Data Mining (ICDM), 2016Co-Authors: Erik Scharwachter, Emmanuel Muller, Jonathan F Donges, Marwan Hassani, Thomas SeidlAbstract:The analysis of the temporal Evolution of dynamic networks is a key challenge for understanding complex processes hidden in graph structured data. Graph Evolution Rules capture such processes on the level of small subgraphs by describing frequently occurring structural changes within a network. Existing Rule discovery methods make restrictive assumptions on the change processes present in networks. We propose EvoMine, a frequent graph Evolution Rule mining method that, for the first time, supports networks with edge insertions and deletions as well as node and edge relabelings. EvoMine defines embedding-based and event-based support as two novel measures to assess the frequency of Rules. These measures are based on novel mappings from dynamic networks to databases of union graphs that retain all Evolution information relevant for Rule mining. Using these mappings the Rule mining problem can be solved by frequent subgraph mining. We evaluate our approach and two baseline algorithms on several real datasets. To the best of our knowledge, this is the first empirical comparison of Rule mining algorithmsfor dynamic networks.
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detecting change processes in dynamic networks by frequent graph Evolution Rule mining
International Conference on Data Mining, 2016Co-Authors: Erik Scharwachter, Emmanuel Muller, Jonathan F Donges, Marwan Hassani, Thomas SeidlAbstract:The analysis of the temporal Evolution of dynamic networks is a key challenge for understanding complex processes hidden in graph structured data. Graph Evolution Rules capture such processes on the level of small subgraphs by describing frequently occurring structural changes within a network. Existing Rule discovery methods make restrictive assumptions on the change processes present in networks. We propose EvoMine, a frequent graph Evolution Rule mining method that, for the first time, supports networks with edge insertions and deletions as well as node and edge relabelings. EvoMine defines embedding-based and event-based support as two novel measures to assess the frequency of Rules. These measures are based on novel mappings from dynamic networks to databases of union graphs that retain all Evolution information relevant for Rule mining. Using these mappings the Rule mining problem can be solved by frequent subgraph mining. We evaluate our approach and two baseline algorithms on several real datasets. To the best of our knowledge, this is the first empirical comparison of Rule mining algorithmsfor dynamic networks.
Emmanuel Muller - One of the best experts on this subject based on the ideXlab platform.
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ICDM - Detecting Change Processes in Dynamic Networks by Frequent Graph Evolution Rule Mining
2016 IEEE 16th International Conference on Data Mining (ICDM), 2016Co-Authors: Erik Scharwachter, Emmanuel Muller, Jonathan F Donges, Marwan Hassani, Thomas SeidlAbstract:The analysis of the temporal Evolution of dynamic networks is a key challenge for understanding complex processes hidden in graph structured data. Graph Evolution Rules capture such processes on the level of small subgraphs by describing frequently occurring structural changes within a network. Existing Rule discovery methods make restrictive assumptions on the change processes present in networks. We propose EvoMine, a frequent graph Evolution Rule mining method that, for the first time, supports networks with edge insertions and deletions as well as node and edge relabelings. EvoMine defines embedding-based and event-based support as two novel measures to assess the frequency of Rules. These measures are based on novel mappings from dynamic networks to databases of union graphs that retain all Evolution information relevant for Rule mining. Using these mappings the Rule mining problem can be solved by frequent subgraph mining. We evaluate our approach and two baseline algorithms on several real datasets. To the best of our knowledge, this is the first empirical comparison of Rule mining algorithmsfor dynamic networks.
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detecting change processes in dynamic networks by frequent graph Evolution Rule mining
International Conference on Data Mining, 2016Co-Authors: Erik Scharwachter, Emmanuel Muller, Jonathan F Donges, Marwan Hassani, Thomas SeidlAbstract:The analysis of the temporal Evolution of dynamic networks is a key challenge for understanding complex processes hidden in graph structured data. Graph Evolution Rules capture such processes on the level of small subgraphs by describing frequently occurring structural changes within a network. Existing Rule discovery methods make restrictive assumptions on the change processes present in networks. We propose EvoMine, a frequent graph Evolution Rule mining method that, for the first time, supports networks with edge insertions and deletions as well as node and edge relabelings. EvoMine defines embedding-based and event-based support as two novel measures to assess the frequency of Rules. These measures are based on novel mappings from dynamic networks to databases of union graphs that retain all Evolution information relevant for Rule mining. Using these mappings the Rule mining problem can be solved by frequent subgraph mining. We evaluate our approach and two baseline algorithms on several real datasets. To the best of our knowledge, this is the first empirical comparison of Rule mining algorithmsfor dynamic networks.