Abstract
In the enterprise context, people need to exploit, interpret and mainly visualize different types of interactions between heterogeneous objects. Graph model is an appropriate way to represent those interactions. Nodes represent the individuals or objects and edges represent the relationships between them. However, extracted graphs are in general heterogeneous and large sized which makes it difficult to visualize and to analyze easily. An adaptive aggregation operation is needed to have more understandable graphs in order to allow users discovering underlying information and hidden relationships between objects. Existing graph summarization approaches such as k-SNAP are carried out in homogeneous graphs where nodes are described by the same list of attributes that represent only one community. The aim of this work is to propose a general tool for graph aggregation which addresses both homogeneous and heterogeneous graphs. To do that, we develop a new soft and adaptive approach to aggregate heterogeneous graphs (i.e., composed of different node attributes and different relationship types) using the definition of Rough Set Theory (RST) combined with Formal Concept Analysis (FCA), the well known K-Medoids and the hierarchical clustering methods. Aggregated graphs are produced according to user-selected node attributes and relationships. To evaluate the quality of the obtained summaries, we propose two quality measures that evaluate respectively the similarity and the separability in groups based on the notion of common neighbor nodes. Experimental results demonstrate that our approach is effective for its ability to produce a high quality solution with relevant interpretations.
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Notes
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In case of multiple central node of second order, we choose randomly one node.
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Louati, A., Aufaure, MA., Cuvelier, E., Pimentel, B. (2016). Soft and Adaptive Aggregation of Heterogeneous Graphs with Heterogeneous Attributes. In: Molli, P., Breslin, J., Vidal, ME. (eds) Semantic Web Collaborative Spaces. SWCS SWCS 2014 2013. Lecture Notes in Computer Science(), vol 9507. Springer, Cham. https://doi.org/10.1007/978-3-319-32667-2_7
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