Extracting information from real-world large networks is a key challenge nowadays. For instance, computing node centrality may become unfeasible depending on the intended due to its computational cost. One solution develop fast methods capable of approximating network centralities. Here, we propose an approach for efficiently centralities using Neural Networks and Graph Embedding techniques. Ou...

Abstract In real-world complex networks, understanding the dynamics of their evolution has been great interest to scientific community. Predicting non-existent but probable links is an essential task social network analysis as addition or removal over time leads evolution. a network, can be categorized intra-community if both end nodes link belong same community, otherwise inter-community links...

Unsupervised graph representation learning aims to distill various information into a downstream task-agnostic dense vector embedding. However, existing approaches are designed mainly under the node homophily assumption: connected nodes tend have similar labels and optimize performance on node-centric tasks. Their design is apparently against principle generally suffers poor in tasks, e.g., edg...

The theoretical computer science community has traditionally used embeddings of finite metrics as a tool in designing approximation algorithms. Recently, however, there has been considerable interest in using metric embeddings in the context of networks to allow network nodes to have more knowledge of the pairwise distances between other nodes in the network. There has also been evidence that n...

We investigate a stochastic model for complex networks, based on a spatial embedding of the nodes, called the Spatial Preferred Attachment (SPA) model. In the SPA model, nodes have spheres of influence of varying size, and new nodes may only link to a node if they fall within its influence region. The spatial embedding of the nodes models the background knowledge or identity of the node, which ...

Embedding network data into a low-dimensional vector space has shown promising performance for many real-world applications, such as node classification and entity retrieval. However, most existing methods focused only on leveraging network structure. For social networks, besides the network structure, there also exists rich information about social actors, such as user profiles of friendship n...

Label Representations • Let l ∈ Rd be the representation of label l, and f be a differentiable embedding function • For labels of label type i, we apply a learnable embedding function l = fi(l) • hi(v) is the embedding of label type i for vertex v: hi(v) = gi ({l | l ∈ labels of type i associated with vertex v}) • h̃i(v) is the reconstruction of the embedding of label type i for vertex v: h̃i(v) ...

We inductively describe an embedding of a complete ternary tree Th of height h into a hypercube Q of dimension at most d(1.6)he + 1 with load 1, dilation 2, node congestion 2 and edge congestion 2. This is an improvement over the known embedding of Th into Q. And it is very close to a conjectured embedding of Havel [3] which states that there exists an embedding of Th into its optimal hypercube...

Graph is an important data representation which appears in a wide diversity of real-world scenarios. Effective graph analytics provides users a deeper understanding of what is behind the data, and thus can benefit a lot of useful applications such as node classification, node recommendation, link prediction, etc. However, most graph analytics methods suffer the high computation and space cost. ...

Different parallel architectures may require different algorithms to make the existent algorithms on one architecture be easily transformed to or implemented on another architecture. This paper proposes a novel algorithm for embedding complete binary trees in a faulty Incrementally Extensible Hypercube (IEH). Furthermore, to obtain the replaceable node of the faulty node, 2-expansion is permitt...