This paper addresses the issue of the security of Internet Coordinate Systems, by proposing a general method for malicious behavior detection during coordinate computations. We first show that the dynamics of a node, in a coordinate system without abnormal or malicious behavior, can be modeled by a Linear State Space model and tracked by a Kalman filter. Then we show, that the obtained model ca...

To explore how related users can optimize the network mining algorithm, author proposes a user algorithm based on fusion of attributes and relationships. This method recommends key technical problems solutions information represented by multi-information explores research associated data algorithms. Research has shown that is 65% higher than previous methods. AUMA-MRL good performance under dif...

The structure of Galton-Watson trees conditioned to be of a given size is wellunderstood. We provide yet another embedding theorem that permits us to obtain asymptotic probabilities of events that are determined by what happens near the root of these trees. As an example, we derive the probability that a Galton-Watson tree is cut when each node is independently removed with probability p, where...

Contrary to the traditional Bag-of-Words approach, we consider the Graph-of-Words (GoW) model in which each document is represented by a graph that encodes relationships between the different terms. Based on this formulation, the importance of a term is determined by weighting the corresponding node in the document, collection and label graphs, using node centrality criteria. We also introduce ...

Head-Internal Relatives (HIRs) in Japanese are regarded as rich context-setters within Dynamic Syntax (DS): the propositional tree of the HIR clause is mapped onto a ‘partial’ tree, which establishes a rich context for the embedding clause to be parsed. This partial tree contains a situation node decorated with the Relevancy restriction and a node for an internal head. This account handles some...

Node representation learning for directed graphs is critically important to facilitate many graph mining tasks. To capture the edges between nodes, existing methods mostly learn two embedding vectors each node, source vector and target vector. However, these separately. For node with very low indegree or outdegree, corresponding cannot be effectively learned. In this paper, we propose a novel D...

OBJECTIVE To explore the morphology and cell component of the sinoatrial node in adult mice and to observe the expressions of neurofilament protein (NF-160) and hyperpolarization-activated cyclic nucleotide regulation of cation channel 4 (HCN4) in sinus node cells. METHODS The right and left atria were collected from 10 adult mice. After paraffin embedding and serial section, these sections u...

In this paper we introduce EvalNE, a Python toolbox for network embedding evaluation. The main goal of EvalNE is to aid researchers and practitioners in performing consistent reproducible evaluations new methods, replicating existing evaluations, conducting benchmark studies. can evaluate models independently their programming language assess the quality learned representations through data vis...

Among the adaptive-grid methods, redistribution and embedding techniques have been the focus of more attention by researchers. Simultaneous or combined adaptive techniques have also been used. This paper describes a combination of adaptive-grid embedding and redistribution methods on semi-structured grids for two-dimensional invisid flows. Since the grid is semi-structured, it is possible to us...

GNNs have been proven to perform highly effectively in various node-level, edge-level, and graph-level prediction tasks several domains. Existing approaches mainly focus on static graphs. However, many graphs change over time their edge may disappear, or the node/edge attribute alter from one other. It is essential consider such evolution representation learning of nodes time-varying In this pa...