With the explosive growth of multi-source data, multi-view clustering has attracted great attention in recent years. Most existing methods operate raw feature space and heavily depend on quality original representation. Moreover, they are often designed for data ignore rich topology structure information. Accordingly, this paper, we propose a generic framework to cluster both attribute graph wi...

Graph representation learning aims at mapping a graph into lower-dimensional feature space. Deep attributed representation, utilizing deep models on the structure and attributes, shows its significance in mining complex relational data. Most existing assume attributes single-attributed view. However, rich information real-world applications demands ability to handle multiple views. For example,...

We propose a new style of writing graph algorithms in functional languages which is based on an alternative view of graphs as inductively deened data types. We show how this graph model can be implemented eeciently, and then we demonstrate how graph algorithms can be succinctly given by recursive function deenitions based on the inductive graph view. We also regard this as a contribution to the...

The complexity of large system models in software engineering nowadays is mastered by using different views. View-based modeling aims at creating small, partial models, each one of them describing some aspect of the system. Existing formal techniques supporting view-based visual modeling are based on typed attributed graphs, where views are related by typed attributed graph morphisms. Such morp...

Abstract This article concerns the problem of a dense mapping system for robot exploring new environment. In this scenario, equipped with an RGB-D camera uses RGB and range data to build consistent model Firstly, requires selection representation. Secondly, has deal localization drift which can be corrected when loop closure is detected. article, we both these problems, make several technical c...

Graph-based multi-view clustering aiming to obtain a partition of data across multiple views, has received considerable attention in recent years. Although great efforts have been made for graph-based clustering, it is still challenging fuse characteristics from various views learn common representation clustering. In this paper, we propose novel Consistent Multiple Graph Embedding Clustering f...

Graph separation is a well-known tool to make (hard) graph problems accessible to a divide-and-conquer approach. We show how to use graph separator theorems in combination with (linear) problem kernels in order to develop fixed parameter algorithms for many wellknown NP-hard (planar) graph problems. We coin the key notion of glueable select&verify graph problems and derive from that a prospecti...

Dear Editor, This letter proposes a contrastive consensus graph learning model for multi-view clustering. Graphs are usually built to outline the correlation between multi-model objects in clustering task, and multiview aims learn that integrates spatial property of each view. Nevertheless, most graph-based models merely consider overall structure from all views but neglect local consistency di...

Recent years have seen a growing number of graph-based semisupervised learning methods. While the literature currently contains several of these methods, their relationships with one another and with other graph-based data analysis algorithms remain unclear. In this paper, we present a unified view of graph-based semi-supervised learning. Our framework unifies three important and seemingly unre...