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...

Zero-shot graph embedding is a major challenge for supervised learning. Although recent method RECT has shown promising performance, its working mechanisms are not clear and still needs lots of training data. In this paper, we give deep insights into RECT, address fundamental limits. We show that core part GNN prototypical model in which class prototype described by mean feature vector. As such...

Neural Architecture Search (NAS) automates and prospers the design of neural networks. Estimator-based NAS has been proposed recently to model relationship between architectures their performance enable scalable flexible search. However, existing estimator-based methods encode architecture into a latent space without considering graph similarity. Ignoring similarity in node-based search may ind...

Recently, the surge in popularity of Internet Things (IoT), mobile devices, social media, etc., has opened up a large source for graph data. Graph embedding been proved extremely useful to learn low-dimensional feature representations from graph-structured These can be used variety prediction tasks node classification link prediction. However, existing methods do not consider users' privacy pre...

Geographic routing is an appealing routing strategy that uses the location information of the nodes to route the data. This technique uses only local information of the communication graph topology and does not require computational effort to build routing table or equivalent data structures. A particularly efficient implementation of this paradigm is greedy routing, where along the data path t...

We study the toroidality testing and toroidal embedding problems in the context of bounded space algorithms. Specifically, we show that the problem of embedding a toroidal graph on a surface of genus 1 can be solved by an algorithm that runs in deterministic logarithmic space. The algorithm rejects whenever the graph is not toroidal. This algorithm is optimal as we also show a matching hardness...