Abstract. In this paper we deal with problems occurring in evolving interrelated Web databanks. Examples of such databanks are networks of interlinked scientific repositories on the Web, managed independently by cooperating research groups. We argue that changes should not be treated solely as transforming operations, but rather as first class citizens retaining structural, semantic and temporal characteristics. We propose a graph model called evo-graph for capturing in a coherent way the inherent relationship between evolving data and changes applied on them. Evo-graph represents changes as arbitrarily complex objects, similarly to data objects. We discuss the temporal characteristics of the evo-graph, and show how the evo-graph can provid...
Real-world complex networks are dynamic in nature and change over time. The change is usually observ...
Many data sources on the Web evolve in the sense that they change their content over time, typically...
Most of the works on learning from networked data assume that the network is static. In this paper w...
Edited by Renata Guizzardi, Anna Perini, Samira CherfiInternational audienceGraph data management sy...
A graph is a mathematical structure for modelling the pairwise relations between objects. This thesi...
Discovery of evolution chains Discovery of change patterns Change mining in networked data a b s t r...
In many applications, information is best represented as graphs. In a dynamic world, information cha...
International audienceThis paper describes a new temporal graph modelling solution to organize and m...
Abstract. The dynamic nature of Web data gives rise to a multitude of problems related to the descri...
Evolving data has attracted considerable research attention. Researchers have focused on modeling an...
Abstract — Graphs are adept at describing relational data, hence their popularity in fields includin...
In this paper we introduce graph-evolution rules, a novel type of frequency-based pattern that descr...
The analysis of the temporal evolution of dynamic networks is a key challenge for understanding comp...
Real-world complex networks are dynamic in nature and change over time. The change is usually observ...
Within the large body of research in complex network analysis, an im-portant topic is the temporal e...
Real-world complex networks are dynamic in nature and change over time. The change is usually observ...
Many data sources on the Web evolve in the sense that they change their content over time, typically...
Most of the works on learning from networked data assume that the network is static. In this paper w...
Edited by Renata Guizzardi, Anna Perini, Samira CherfiInternational audienceGraph data management sy...
A graph is a mathematical structure for modelling the pairwise relations between objects. This thesi...
Discovery of evolution chains Discovery of change patterns Change mining in networked data a b s t r...
In many applications, information is best represented as graphs. In a dynamic world, information cha...
International audienceThis paper describes a new temporal graph modelling solution to organize and m...
Abstract. The dynamic nature of Web data gives rise to a multitude of problems related to the descri...
Evolving data has attracted considerable research attention. Researchers have focused on modeling an...
Abstract — Graphs are adept at describing relational data, hence their popularity in fields includin...
In this paper we introduce graph-evolution rules, a novel type of frequency-based pattern that descr...
The analysis of the temporal evolution of dynamic networks is a key challenge for understanding comp...
Real-world complex networks are dynamic in nature and change over time. The change is usually observ...
Within the large body of research in complex network analysis, an im-portant topic is the temporal e...
Real-world complex networks are dynamic in nature and change over time. The change is usually observ...
Many data sources on the Web evolve in the sense that they change their content over time, typically...
Most of the works on learning from networked data assume that the network is static. In this paper w...
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