Subspace clustering seeks to cluster high-dimensional data lying in a union of low-dimensional subspaces. It has achieved state-of-the-art results in image clustering, but text clustering of document-term matrices, has proved more impervious to advances with this approach, even though text data satisfies the assumptions of subspace clustering. We hypothesize that this is because such matrices are generally sparser and higher-dimensional than images. This, combined with the complexity of subspace clustering, which is generally cubic in the number of inputs, makes its use impractical in the context of text. Here we address these issues with a view to leveraging subspace clustering for networked (or not) text data. We first extend the concept of subspace clustering to co-clustering, which is suitable to deal with document-term matrices because of the interplay engendered between the document and word representations. We then address the sparsity problem through bilateral graph convolution, which promotes the grouping effect that has been credited for the effectiveness of some subspace clustering models. The proposed formulation results in an algorithm that is computationally/spatially efficient. Experiments using real-world datasets demonstrate the superior performance, in terms of document clustering, word clustering, and computational efficiency, of our proposed approach over the baselines and comparable methods.

Over recent years, graph convolutional networks emerged as powerful node clustering methods and have set state of the art results for this task. In this paper, we argue that some of these methods are unnecessarily complex and propose a node clustering model that is more scalable while being more effective. The proposed model uses Laplacian smoothing to learn an initial representation of the graph before applying an efficient self-expressive subspace clustering procedure. This is performed via learning a factored coefficient matrix. These factors are then embedded into a new feature space in such a way as to generate a valid affinity matrix (symmetric and non-negative) on which an implicit spectral clustering algorithm is performed. Experiments on several real-world attributed datasets demonstrate the cost-effective nature of our method with respect to the state of the art.

Over the last few years, various multi-view graph clustering methods have shown promising performances. However, we argue that these methods can have limitations. In particular, they are often unnecessarily complex, leading to scalability problems that make them prohibitive for most real-world graph applications. Furthermore, many of them can handle only specific types of multi-view graphs. Another limitation is that the process of learning graph representations is separated from the clustering process, and in some cases these methods do not even learn a graph representation, which severely restricts their flexibility and usefulness. In this paper we propose a simple yet effective linear model that addresses the dual tasks of multi-view attributed graph representation learning and clustering in a unified framework. The model starts by performing a first-order neighborhood smoothing step for the different individual views, then gives each one a weight corresponding to its importance. Finally, an iterative process of simultaneous clustering and representation learning is performed w.r.t. the importance of each view, yielding a consensus embedding and partition of the graph. Our model is generic and can deal with any type of multi-view graph. Finally, we show through extensive experimentation that this simple model consistently achieves competitive performances w.r.t. state-of-the-art multi-view attributed graph clustering models, while at the same time having training times that are shorter, in some cases by orders of magnitude.

Subspace clustering aims to cluster high dimensional data lying in a union of low-dimensional subspaces. It has shown good results on the task of image clustering but text clustering, using document-term matrices, proved more impervious to advances based on this approach. We hypothesize that this is because, compared to image data, text data is generally higher dimensional and sparser. This renders subspace clustering impractical in such a context. Here, we leverage subspace clustering for text by addressing these issues. We first extend the concept of subspace clustering to co-clustering, which has been extensively used on document-term matrices due to the resulting interplay between the document and term representations. We then address the sparsity problem through a two-way graph convolution, which promotes the grouping effect that has been credited for the effectiveness of some subspace clustering models. The proposed formulation results in an algorithm that is efficient both in terms of computational and spatial complexity. We show the competitiveness of our model w.r.t the state-of-the-art on document-term attributed graph datasets in terms of performance and efficiency.

Bipartite graphs can be used to model a wide variety of dyadic information such as user-rating, document-term, and gene-disorder pairs. Biclustering is an extension of clustering to the underlying bipartite graph induced from this kind of data. In this paper, we leverage optimal transport (OT) which has gained momentum in the machine learning community to propose a novel and scalable biclustering model that generalizes several classical biclustering approaches. We perform extensive experimentation to show the validity of our approach compared to other OT biclustering algorithms along both dimensions of the dyadic datasets.

Attributed graphs are used to model a wide variety of real-world networks. Recent graph convolutional network-based representation learning methods have set state-of-the-art results on the clustering of attributed graphs. However, these approaches deal with clustering as a downstream task while better performances can be attained by incorporating the clustering objective into the representation learning process. In this paper, we propose, in a unified framework, an objective function taking into account both tasks simultaneously. Based on a variant of the simple graph convolutional network, our model does clustering by minimizing the difference between the convolved node representations and their reconstructed cluster representatives. We showcase the efficiency of the derived algorithm against state-of-the-art methods both in terms of clustering performance and computational cost on thede facto benchmark graph clustering datasets. We further demonstrate the usefulness of the proposed approach for graph visualization through generating embeddings that exhibit a clustering structure.