Abstract Given a node-attributed graph, how can we efficiently represent it with few numerical features that expressively reflect its topology and attribute information? We propose A-DOGE , for attributed DOS-based graph embedding, based on density of states (DOS, a.k.a. spectral density) to tackle this problem. is designed fulfill long desiderata desirable characteristics. Most notably, capita...

There has lately been increased interest in describing complex systems not merely as single networks but rather as collections of networks that are coupled to one another in different ways. Here we introduce a tractable model for embedding one network (A) into another (B), focusing on the case where network A has many more nodes than network B. In our model, nodes in network A are assigned, or ...

Automatic feature learning algorithms are at the forefront of modern day machine learning research. We present a novel algorithm, supervised Q-walk, which applies Q-learning to generate random walks on graphs such that the walks prove to be useful for learning node features suitable for tackling with the node classification problem. We present another novel algorithm, k-hops neighborhood based ...

Several network embedding models have been developed for unsigned networks. However, these models based on skip-gram cannot be applied to signed networks because they can only deal with one type of link. In this paper, we present our signed network embedding model called SNE. Our SNE adopts the log-bilinear model, uses node representations of all nodes along a given path, and further incorporat...

Graph-embedding learning is the foundation of complex information network analysis, aiming to represent nodes in a graph as low-dimensional dense real-valued vectors for application practical analysis tasks. In recent years, study representation has received increasing attention from researchers, and, among them, neural networks (GNNs) based on deep are playing an increasingly important role th...

Abs-t-We study the problem of running full binary tree based algorithms on a hypercube with faulty nodes. The key to this problem is to devise a method for embedding a full binary tree into the faulty hypercube. Based on a novel embedding strategy, we present two results for embedding an (n-1)-tree (a full binary tree with 2"-l-1 nodes) into an n-cube (a hypercube with 2" nodes) with unit dilat...