Finding High-Order Homologous Microbe Community Modules via Network Embedding

Temporal network embedding, which aims to learn the low-dimensional representations of nodes in temporal networks that can capture and preserve the network structure and evolution pattern, has attracted much attention from the scientific community. However, existing methods suffer from two main disadvantages: 1) they cannot preserve the node temporal proximity that capture important properties of the network structure; and 2) they cannot represent the nonlinear structure of temporal networks. In this paper, we propose a high-order nonlinear information preserving (HNIP) embedding method to address these issues. Specifically, we define three orders of temporal proximities by exploring network historical information with a time exponential decay model to quantify the temporal proximity between nodes. Then, we propose a novel deep guided auto-encoder to capture the highly nonlinear structure. Meanwhile, the training set of the guide auto-encoder is generated by the temporal random walk (TRW) algorithm. By training the proposed deep guided auto-encoder with a specific mini-batch stochastic gradient descent algorithm, HNIP can efficiently preserves the temporal proximities and highly nonlinear structure of temporal networks. Experimental results on four real-world networks demonstrate the effectiveness of the proposed method.

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Path Aggregation Model for Attributed Network Embedding with a Smart Random Walk

Journal of Physics Conference Series ◽

10.1088/1742-6596/2082/1/012011 ◽

2021 ◽

Vol 2082 (1) ◽

pp. 012011

Author(s):

Xiang Xiao ◽

Kang Zhang ◽

Shuang Qiu ◽

Wei Liu

Keyword(s):

Random Walk ◽

High Order ◽

Edge Weight ◽

Task Graph ◽

Network Embedding ◽

Convolutional Network ◽

Aggregation Model ◽

Sample Paths ◽

Attributed Network ◽

Walk Algorithm

Abstract Network embedding has attracted a surge of attention recently. In this field, how to preserve high-order proximity has long been a difficult task. Graph convolutional network (GCN) and random walk-based approaches can preserve high-order proximity to a certain extent. However, they partially concentrate on the aggregation process and sampling process respectively. Path aggregation methods combine the merits of GCN and random walk, and thus can preserve more high-order information and achieve better performance. However, path aggregation framework has not been applied in attributed network embedding yet. In this paper, we propose a path aggregation model for attributed network embedding, with two main contributions. First, we claim that there always exists implicit edge weight in networks, and design a tweaked random walk algorithm to sample paths accordingly. Second, we propose a path aggregation framework dealing with both nodes and attributes. Extensive experimental results show that our proposal outperforms the cutting-edge baselines on downstream tasks, such as node clustering, node classification, and link prediction.

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BHONEM: Binary High-Order Network Embedding Methods for Networked-Guarantee Loans

Journal of Computer Science and Technology ◽

10.1007/s11390-019-1934-8 ◽

2019 ◽

Vol 34 (3) ◽

pp. 657-669 ◽

Cited By ~ 2

Author(s):

Da-Wei Cheng ◽

Yi Tu ◽

Zhen-Wei Ma ◽

Zhi-Bin Niu ◽

Li-Qing Zhang

Keyword(s):

High Order ◽

Network Embedding ◽

Embedding Methods

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Multiplex Network Embedding Model with High-Order Node Dependence

Complexity ◽

10.1155/2021/6644111 ◽

2021 ◽

Vol 2021 ◽

pp. 1-18

Author(s):

Nianwen Ning ◽

Qiuyue Li ◽

Kai Zhao ◽

Bin Wu

Keyword(s):

Real World ◽

Information Diffusion ◽

Structural Information ◽

High Order ◽

Network Embedding ◽

Multiplex Networks ◽

Cross Domain ◽

Multiplex Network ◽

Node Attributes ◽

Two Phases

Multiplex networks have been widely used in information diffusion, social networks, transport, and biology multiomics. They contain multiple types of relations between nodes, in which each type of the relation is intuitively modeled as one layer. In the real world, the formation of a type of relations may only depend on some attribute elements of nodes. Most existing multiplex network embedding methods only focus on intralayer and interlayer structural information while neglecting this dependence between node attributes and the topology of each layer. Attributes that are irrelevant to the network structure could affect the embedding quality of multiplex networks. To address this problem, we propose a novel multiplex network embedding model with high-order node dependence, called HMNE. HMNE simultaneously considers three properties: (1) intralayer high-order proximity of nodes, (2) interlayer dependence in respect of nodes, and (3) the dependence between node attributes and the topology of each layer. In the intralayer embedding phase, we present a symmetric graph convolution-deconvolution model to embed high-order proximity information as the intralayer embedding of nodes in an unsupervised manner. In the interlayer embedding phase, we estimate the local structural complementarity of nodes as an embedding constraint of interlayer dependence. Through these two phases, we can achieve the disentangled representation of node attributes, which can be treated as fined-grained semantic dependence on the topology of each layer. In the restructure phase of node attributes, we perform a linear fusion of attribute disentangled representations for each node as a reconstruction of original attributes. Extensive experiments have been conducted on six real-world networks. The experimental results demonstrate that the proposed model outperforms the state-of-the-art methods in cross-domain link prediction and shared community detection tasks.

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Decision letter for "A crabs’ high‐order brain center resolved as a mushroom body‐like structure"

10.1002/cne.24960/v2/decision1 ◽

2020 ◽

Keyword(s):

Mushroom Body ◽

High Order ◽

Brain Center

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Review for "A crabs’ high‐order brain center resolved as a mushroom body‐like structure"

10.1002/cne.24960/v1/review1 ◽

2020 ◽

Keyword(s):

Mushroom Body ◽

High Order ◽

Brain Center

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Determination of the Burgers vector of a dislocation in a Fe-Mn alloy by weak-beam image in HVEM

Proceedings, annual meeting, Electron Microscopy Society of America ◽

10.1017/s0424820100108799 ◽

1978 ◽

Vol 36 (1) ◽

pp. 330-331

Author(s):

Y. Ishida ◽

H. Ishida ◽

K. Kohra ◽

H. Ichinose

Keyword(s):

High Order ◽

Simulation Technique ◽

The Other ◽

Image Simulation ◽

Diffraction Vector ◽

Burgers Vector ◽

Weak Beam ◽

Beam Image ◽

Edge Component

IntroductionA simple and accurate technique to determine the Burgers vector of a dislocation has become feasible with the advent of HVEM. The conventional image vanishing technique(1) using Bragg conditions with the diffraction vector perpendicular to the Burgers vector suffers from various drawbacks; The dislocation image appears even when the g.b = 0 criterion is satisfied, if the edge component of the dislocation is large. On the other hand, the image disappears for certain high order diffractions even when g.b ≠ 0. Furthermore, the determination of the magnitude of the Burgers vector is not easy with the criterion. Recent image simulation technique is free from the ambiguities but require too many parameters for the computation. The weak-beam “fringe counting” technique investigated in the present study is immune from the problems. Even the magnitude of the Burgers vector is determined from the number of the terminating thickness fringes at the exit of the dislocation in wedge shaped foil surfaces.

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The usefulness of high index low symmetry zone axes for holz line analysis

Proceedings, annual meeting, Electron Microscopy Society of America ◽

10.1017/s0424820100126718 ◽

1987 ◽

Vol 45 ◽

pp. 384-385

Author(s):

C. M. Sung ◽

D. B. Williams

Keyword(s):

Lattice Parameter ◽

High Order ◽

Zone Axis ◽

High Index ◽

High Symmetry ◽

Second Phases ◽

Line Patterns ◽

Low Symmetry ◽

Line Analysis

Researchers have tended to use high symmetry zone axes (e.g. <111> <114>) for High Order Laue Zone (HOLZ) line analysis since Jones et al reported the origin of HOLZ lines and described some of their applications. But it is not always easy to find HOLZ lines from a specific high symmetry zone axis during microscope operation, especially from second phases on a scale of tens of nanometers. Therefore it would be very convenient if we can use HOLZ lines from low symmetry zone axes and simulate these patterns in order to measure lattice parameter changes through HOLZ line shifts. HOLZ patterns of high index low symmetry zone axes are shown in Fig. 1, which were obtained from pure Al at -186°C using a double tilt cooling holder. Their corresponding simulated HOLZ line patterns are shown along with ten other low symmetry orientations in Fig. 2. The simulations were based upon kinematical diffraction conditions.

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Are HOLZ lines kinematic in off-zone-axis orientation?

Proceedings, annual meeting, Electron Microscopy Society of America ◽

10.1017/s0424820100149295 ◽

1993 ◽

Vol 51 ◽

pp. 692-693

Author(s):

J. M. Zuo ◽

A. L. Weickenmeier ◽

R. Holmestad ◽

J. C. H. Spence

Keyword(s):

Structure Determination ◽

Standard Procedure ◽

High Order ◽

Zone Axis ◽

Great Promise ◽

Line Position ◽

Measured Intensity ◽

Constant Intensity ◽

Axis Orientation ◽

Diffraction Condition

The application of high order reflections in a weak diffraction condition off the zone axis center, including those in high order laue zones (HOLZ), holds great promise for structure determination using convergent beam electron diffraction (CBED). It is believed that in this case the intensities of high order reflections are kinematic or two-beam like. Hence, the measured intensity can be related to the structure factor amplitude. Then the standard procedure of structure determination in crystallography may be used for solving unknown structures. The dynamic effect on HOLZ line position and intensity in a strongly diffracting zone axis is well known. In a weak diffraction condition, the HOLZ line position may be approximated by the kinematic position, however, it is not clear whether this is also true for HOLZ intensities. The HOLZ lines, as they appear in CBED patterns, do show strong intensity variations along the line especially near the crossing of two lines, rather than constant intensity along the Bragg condition as predicted by kinematic or two beam theory.

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