background: sentinel lymph node biopsy (slnb) has replaced axillary lymph node dissection (alnd) in early breast cancer patients as the first line surgical approach to axillary nodes. further dissection is performed only when sln is involved by tumor cells. however, in a significant proportion of patients, non-sentinel nodes are still not involved and axillary dissection has no additional thera...

Abstract Network embedding aims to map nodes in a network low-dimensional vector representations. Graph neural networks (GNNs) have received much attention and achieved state-of-the-art performance learning node representation. Using fundamental sociological theories (status theory balance theory) model signed networks, basing GNN on has become hot topic embedding. However, most GNNs fail use e...

A robust worldwide air-transportation network (WAN) is one that minimizes the number of stranded passengers under a sequence of airport closures. Building on top of this realistic example, here we address how spatial network robustness can profit from cooperation between local actors. We swap a series of links within a certain distance, a cooperation range, while following typical constraints o...

Abstract Attributed graph embedding aims to learn node representation based on the topology and attributes. The current mainstream GNN-based methods of target by aggregating attributes its neighbor nodes. These still face two challenges: (1) In neighborhood aggregation procedure, each would be propagated neighborhoods which may cause disturbance original over-smoothing in GNN iteration. (2) Bec...

Graph embedding, which constructs vector representation of nodes in a network, has shown effectiveness many graph analysis tasks, such as node classification, clustering, and link prediction. However, due to the complexity embedding models (GEMs) their nontransparency hyperparameters, evaluation comparison results retaining original features, consequently, selection suitable GEMs according task...

Role-based network embedding aims to embed role-similar nodes into a similar space, which is widely used in graph mining tasks such as role classification and detection. Roles are sets of networks with structural patterns functions. However, the may be far away or even disconnected from each other. Meanwhile, neighborhood node features noise also affect result role-based embedding, challenges c...

Scale-free networks have been studied mostly as non-spatially embedded systems. However, in many realistic cases, they are spatially embedded and these constraints should be considered. Here, we study the structural and functional properties of a model of scale-free (SF) spatially embedded networks. In our model, both the degree and the length of links follow power law distributions as found in...

Node importance evaluation is a hot issue in complex network analysis. Existing node methods are mainly oriented to homogeneous networks, which ignore the heterogeneity of types and edges. We propose an MLN critical method based on graph convolution. In this paper, we generate feature matrix nodes. Considering diversity network, design adapted sampling meta path. An embedding model constructed ...

Network virtualization is a technology that enables multiple virtual instances to coexist on a common physical network infrastructure. This paradigm fostered new business models, allowing infrastructure providers to lease or share their physical resources. Each virtual network is isolated and can be customized to support a new class of customers and applications. To this end, infrastructure pro...

We present an efficient algorithm for a problem in the interface between clustering and graph embeddings. An embedding φ : G→M of a graph G into a 2-manifold M maps the vertices in V (G) to distinct points and the edges in E(G) to interior-disjoint Jordan arcs between the corresponding vertices. In applications in clustering, cartography, and visualization, nearby vertices and edges are often b...