Abstract We present network embedding algorithms that capture information about a node from the local distribution over attributes around it, as observed random walks following an approach similar to Skip-gram. Observations neighbourhoods of different sizes are either pooled (AE) or encoded distinctly in multi-scale (MUSAE). Capturing attribute-neighbourhood relationships multiple scales is use...

In this paper, we introduce a new setting for graph embedding, which considers embedding communities instead of individual nodes. Community embedding is useful as a natural community representation for applications, and it provides an exciting opportunity to improve community detection. Specifically, we see the interaction between community embedding and detection as a closed loop, through node...

Embedding graph nodes into a vector space can allow the use ofmachine learning to e.g. predict node classes, but the study of node embedding algorithms is immature compared to the natural language processing field because of a diverse nature of graphs. We examine the performance of node embedding algorithms with respect to graph centrality measures that characterize diverse graphs, through syst...

Background: The link prediction issue is one of the most widely used problems in complex network analysis. Link prediction requires knowing the background of previous link connections and combining them with available information. The link prediction local approaches with node structure objectives are fast in case of speed but are not accurate enough. On the other hand, the global link predicti...

Signed network embedding methods aim to learn vector representations of nodes in signed networks. However, existing algorithms only managed embed networks into low-dimensional Euclidean spaces whereas many intrinsic features are reported more suitable for non-Euclidean spaces. For instance, previous works did not consider the hierarchical structures networks, which is widely witnessed real-worl...

Representation learning using network embedding has received tremendous attention due to its efficacy solve downstream tasks. Popular methods (such as deepwalk, node2vec, LINE) are based on a neural architecture, thus unable scale large networks both in terms of time and space usage. Recently, we proposed BinSketch, sketching technique for compressing binary vectors vectors. In this paper, show...

Network node embedding is an active research subfield of complex network analysis. This paper contributes a novel approach to learning embeddings and direct classification using ranking scheme coupled with autoencoder-based neural architecture. The main advantages the proposed Deep Node Ranking (DNR) algorithm are competitive or better performance, significantly higher speed lower space require...

Network embedding is a classical task which aims to map the nodes of a network to lowdimensional vectors. Most of the previous network embedding methods are trained in an unsupervised scheme. Then the learned node embeddings can be used as inputs of many machine learning tasks such as node classification, attribute inference. However, the discriminant power of the node embeddings maybe improved...