Dealing with relational learning generally relies on tools modeling relational data. An undirected graph can represent these data with vertices depicting entities and edges describing the relationships between the entities. These relationships can be well represented by multiple undirected graphs over the same set of vertices with edges arising from different graphs catching heterogeneous relations. The vertices of those networks are often structured in unknown clusters with varying properties of connectivity. These multiple graphs can be structured as a three-way tensor, where each slice of tensor depicts a graph which is represented by a count data matrix. To extract relevant clusters, we propose an appropriate model-based co-clustering capable of dealing with multiple graphs. The proposed model can be seen as a suitable tensor extension of mixture models of graphs, while the obtained co-clustering can be treated as a consensus clustering of nodes from multiple graphs. Applications on real datasets and comparisons with multi-view clustering and tensor decomposition methods show the interest of our contribution.

处理关系学习通常依赖于对关系数据建模的工具。无向图可以用描述实体的顶点和描述实体之间关系的边来表示这些数据。这些关系可以通过在同一组顶点上的多个无向图很好地表示，这些无向图的边来自不同的图，这些图捕获了异构关系。这些网络的顶点通常由具有不同连接特性的未知集群构成。这些多个图可以构造为一个三向张量，其中每个张量切片描绘了一个由计数数据矩阵表示的图。为了提取相关集群，我们提出了一种能够处理多个图的适当的基于模型的协同集群。所提出的模型可以看作是图的混合模型的合适张量扩展，而获得的共聚类可以视为来自多个图的节点的共识聚类。在真实数据集上的应用以及与多视图聚类和张量分解方法的比较显示了我们贡献的兴趣。