JZTXT

graph

Occupancy Grid Map to Pose Graph-based Map: Robust BIM-based 2D- LiDAR Localization for Lifelong Indoor Navigation in Changing and Dynamic Environments

将占据栅格地图转换为基于姿态图的地图：基于BIM的2D LiDAR定位在变化和动态环境中实现终身室内导航的鲁棒性。 摘要： 许多研究都依赖于事实上的标准自适应蒙特卡罗定位（AMCL）方法，以在从建筑信息模型（BIM模型）提取的占用栅格地图（OGM）中定位机器人。然而，大多数这些研究都假设BIM模型准 ......
based Environments Localization Graph-based Navigation

CF323B - Tournament-Graph

题意：构造一个 $n$ 大小的锦标赛图，即每两点之间恰有一条有向边，满足任意点对 $(u,v)$，都存在一条从 $u$ 到 $v$，长度不超过 $2$ 的路径。 #### 方法一 考虑奇数情况，假设我们的点是在环上排列的，那么我们对任意的跨越不超过半个环的边都连上，也就是说，我们把点看成圆上的若干个 ......
Tournament-Graph Tournament Graph 323B 323

Re: finding all cycles in a graph

ref: https://cs.stackexchange.com/questions/7216/find-the-simple-cycles-in-a-directed-graph Re: finding all cycles in a graph From: Juan Pablo Carbaja ......
finding cycles graph all Re

DIFFormer Scalable (Graph) Transformers Induced by Energy Constrained Diffusion

[TOC] > [Wu Q., Yang C., Zhao W., He Y., Wipf D. and Yan J. DIFFormer: Scalable (graph) transformers induced by energy constrained diffusion. ICLR, 20 ......
Transformers Constrained DIFFormer Diffusion Scalable

Learning to Pre-train Graph Neural Networks 学习如何预训练GNN

![image](https://img2023.cnblogs.com/blog/2992171/202306/2992171-20230607143536765-414002095.png) ![image](https://img2023.cnblogs.com/blog/2992171/20 ......
Pre-train Learning Networks Neural Graph

Self-Supervised Graph Co-Training for Session-based Recommendation

[TOC] > [Xia X., Yin H., Yu J., Shao Y. and Cui L. Self-supervised graph co-training for session-based recommendation. CIKM, 2021.](http://arxiv.org/a ......
Self-Supervised Recommendation Session-based Co-Training Supervised

Global Context Enhanced Graph Neural Networks for Session-based Recommendation

[TOC] > [Wang Z., Wei W., Cong G., Li X., Mao X. and Qiu M. Global context enhanced graph neural networks for session-based recommendation. SIGIR, 202 ......
Recommendation Session-based Enhanced Networks Context

CF506D - Mr. Kitayuta's Colorful Graph

本质不同的算法主要有两种：对子图大小根号分治和类启发式均摊。此外还有很多实现上的差别。 #### 对子图大小根号分治 在线做法： 我们发现，把每个颜色的边和它们的顶点取出为一个子图，所有子图大小的和是 $O(n)$ 级别的。那么我们就可以根号分治。 首先，要预处理每个颜色子图下的连通块。可以用并查集 ......
Kitayuta Colorful Graph 506D 506

转载-奇小葩-深入ftrace function graph原理

原文链接：https://blog.csdn.net/u012489236/article/details/127838701 学习完了ftrace的function的基本功能，其作用主要是用来跟踪特定内核函数调用的频次，对于内核，特别是初学者，对于函数的调用关系不清晰，并且内核中有很多函数指针，会 ......
function 原理 ftrace graph

AtCoder Regular Contest 161 E Not Dyed by Majority (Cubic Graph)

[洛谷传送门](https://www.luogu.com.cn/problem/AT_arc161_e "洛谷传送门") [AtCoder 传送门](https://atcoder.jp/contests/arc161/tasks/arc161_e "AtCoder 传送门") 给构造题提供了一种 ......
Majority AtCoder Regular Contest Cubic

The Open Graph protocol(开放图谱协议)的介绍及应用

### 介绍 `Open Graph 协议`使任何网页都可以成为社交中的丰富对象。例如，用于 `Facebook` 以允许任何网页具有与 `Facebook `上任何其他对象相同的功能。 以下是把链接分享到`钉钉`，钉钉识别后显示的效果： ![](https://oss.milovetingting ......
图谱 protocol Graph Open The

Permutation Invariant Graph Generation via Score-Based Generative Modeling

[TOC] > [Niu C., Song Y., Song J., Zhao S., Grover A. and Ermon S. Permutation invariant graph generation via score-based generative modeling. AISTATS ......
Permutation Score-Based Generation Generative Invariant

Efficient Graph Generation with Graph Recurrent Attention Networks

[TOC] > [Liao R., Li Y., Song Y., Wang S., Nash C., Hamilton W. L., Duvenaud D., Urtasun R. and Zemel R. NIPS, 2019.](http://arxiv.org/abs/1910.00760) ......
Graph Generation Efficient Attention Recurrent

B. Complete The Graph

B. Complete The Graph ZS the Coder has drawn an undirected graph of $n$ vertices numbered from $0$ to $n - 1$ and $m$ edges between them. Each edge of ......
Complete Graph The

Graph Normalizing Flows

[TOC] > [Liu J., Kumar A., Ba J., Kiros J. and Swersky K. Graph normalizing flows. NIPS, 2019.](http://arxiv.org/abs/1905.13177) ## 概 基于 [flows](https ......
Normalizing Graph Flows

Graph Embedding：LINE算法

背景 如上图所示，结点6和7是相邻结点，他们应该是相似结点，结点5和6虽然不是相邻结点，但是它们有共同的相邻的结点，因此它们也应该是相似结点。 基于词观察，LINE算法提出了一阶相似性算法和二阶相似性算法 First-order 我们首先如如下公式来计算结点i和j的联合概率分布： 其中ui，uj​分 ......
算法 Embedding Graph LINE

Paper Reading: forgeNet a graph deep neural network model using tree-based ensemble classifiers for feature graph construction

[toc] Paper Reading 是从个人角度进行的一些总结分享，受到个人关注点的侧重和实力所限，可能有理解不到位的地方。具体的细节还需要以原文的内容为准，博客中的图表若未另外说明则均来自原文。 | 论文概况 | 详细 | | | | | 标题 | 《forgeNet: a graph dee ......
graph construction classifiers tree-based forgeNet

经典的Graph Embedding方法：DeepWalk 和 Node2vec

DeepWalk Deep Walk，它是 2014 年由美国石溪大学的研究者提出的。它的主要思想是在由物品组成的图结构上进行随机游走，产生大量物品序列，然后将这些物品序列作为训练样本输入 Word2vec 进行训练，最终得到物品的 Embedding Node2vec 2016 年，斯坦福大学的研 ......
Embedding DeepWalk Node2vec 方法 经典

Combining Label Propagation and Simple Models Out-performs Graph Neural Networks

[TOC] > [Huang Q., He H., Singh A., Lim S. and Benson A. R. Combining label propagation and simple models out-performs graph neural networks. ICLR, 20 ......
Out-performs Propagation Combining Networks performs

POJ1737 Connected Graph ( n点无向连通图计数

题意说明:求 $n$ 个点的无向连通图个数 据说已经非常典了,但是我太菜了不会组合数学,最近补档时看到这道题,决定记录下来理理思路 ![image](https://img2023.cnblogs.com/blog/3146663/202305/3146663-20230520234501796-1 ......
Connected Graph 1737 POJ

基于Graph-Cut算法的彩色图像深度信息提取matlab仿真

1.算法仿真效果 matlab2022a仿真结果如下： 2.算法涉及理论知识概要 Graph cuts是一种十分有用和流行的能量优化算法，在图像处理领域普遍应用于前后背景分割（Image segmentation）、立体视觉（stereo vision）、抠图（Image matting）等，目前在 ......
算法 Graph-Cut 深度 图像 彩色

Do Transformers Really Perform Badly for Graph Representation

Ying C., Cai T., Luo S., Zheng S., Ke D., Shen Y. and Liu T. Do transformers really perform badly for graph representation? NIPS, 2021. 概 本文提出了一种基于图的 ......
Representation Transformers Perform Really Badly

Understanding Structural Vulnerability in Graph Convolutional Networks

Chen L., Li J., Peng Q., Liu Y., Zheng Z. and Yang C. Understanding structural vulnerability in graph convolutional networks. IJCAI, 2021. 概 mean 是在 G ......
Understanding Convolutional Vulnerability Structural Networks

MBN:Mutual Boost Network for Attributed Graph Clustering

论文阅读07-MBN:Mutual Boost Network for Attributed Graph Clustering 论文信息 论文地址：https://papers.ssrn.com/sol3/papers.cfm?abstract_id=4195979 代码地址：https://git ......
Attributed Clustering Network Mutual Boost

论文解读《Mixup for Node and Graph Classification》

论文信息 论文标题：Mixup for Node and Graph Classification论文作者：Yiwei Wang、Wei Wang论文来源：WWW 2021论文地址：download 论文代码：download视屏讲解：click 1 介绍 ......
Classification 论文 Mixup Graph Node

论文解读（ID-MixGCL）《ID-MixGCL: Identity Mixup for Graph Contrastive Learning》

论文信息 论文标题：ID-MixGCL: Identity Mixup for Graph Contrastive Learning论文作者：Gehang Zhang.....论文来源：2023 aRxiv论文地址：download 论文代码：download视屏讲解：click 介绍 ......
ID-MixGCL MixGCL Contrastive Identity Learning

D. Fish Graph

D. Fish Graph You are given a simple undirected graph with $n$ nodes and $m$ edges. Note that the graph is not necessarily connected. The nodes are la ......
Graph Fish

Graph Convolutional Networks with EigenPooling

Ma Y., Wang S., Aggarwal C. C. and Tang J. Graph convolutional networks with eigenpooling. KDD, 2019. 概 本文提出了一种新的框架, 在前向的过程中, 可以逐步将相似的 nodes 和他们的特征聚合在 ......
Convolutional EigenPooling Networks Graph with

Handling Information Loss of Graph Neural Networks for Session-based Recommendation

Chen T. and Wong R. C. Handling information loss of graph neural networks for session-based recommendation. KDD, 2020. 概 作者发现图用在 Session 推荐中存在: lossy ......
Recommendation Session-based Information Handling Networks

B. Greg and Graph

题目 B. Greg and Graph 题意 输入 n(1≤n≤500) 表示 n 个点的有向完全图，然后输入 n*n 的邻接矩阵 a，其中 a[i][j] 表示 i 到 j 的边权，范围 [1,1e5]（特例是 a[i][i]=0）。 图的节点编号从 1 开始。 然后输入 1~n 的排列，表示我 ......
Graph Greg and
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