Optimal Graph Filters for Clustering Attributed Graphs

The second class of methods uses graph embedding, Many real-world systems can be represented as graphs where the in particular graph autoencoders. Graph convolutional network different entities are presented by nodes and their interactions by (GCN) based methods such as Graph autoencoders (GAE) [13], variational edges. An important task in studying large datasets is graph clustering. GAE (VGAE) [14], adversarially regularized graph autoencoder While there has been a lot of work on graph clustering using the (ARGA), adversarially regularized variational graph autoencoder connectivity between the nodes, many real-world networks also have (ARVGA) [15] and marginalized graph autoencoder for graph node attributes. Clustering attributed graphs requires joint modeling clustering (MGAE) [16] have demonstrated state-of-the-art performance of graph structure and node attributes. Recent work has focused on on several attributed graph clustering tasks. These methods graph convolutional networks and graph convolutional filters to combine directly use GCN as a feature extractor, where each convolutional structural and content information. However, these methods are layer is equivalent to a first-order low-pass graph filter that only takes mostly limited to lowpass filtering and do not explicitly optimize the the most immediate neighbors of each node into account.